What Is Cloud Hosting? A Comprehensive Guide

Cloud hosting is a modern approach to deploying and managing websites, applications, and data using virtualized computing resources spread across multiple servers in the cloud. The model allows businesses to quickly adjust their resource usage based on real-time demand, avoid infrastructure limitations, and pay only for what they consume. As a result, cloud hosting provides the flexibility and reliability needed to support today’s dynamic online services and fast-growing digital ecosystems.

This article explains cloud hosting and its key features, types, and the benefits of implementing cloud hosting architectures into your daily operations.

What Is Cloud Hosting?

Cloud hosting enables websites, applications, and services to run on virtual servers that draw their resources from a large pool of physical machines in a cloud provider’s data centers. Instead of renting a single physical server, you consume compute, memory, storage, and networking as abstracted resources, typically delivered via virtualization or containers.

Customers usually pay per hour or per second of compute, per GB of storage, and per GB of data transfer, rather than fixed monthly server rentals. This makes cloud hosting well-suited for variable or unpredictable workloads. The workloads include CI/CD-driven deployments, microservices architectures, and global applications that need to be close to users in multiple regions.

To ensure the best option for hosting your business operations, check out our article that compares colocation and cloud hosting.

How Does Cloud Hosting Work?

Cloud hosting abstracts physical infrastructure into flexible, on-demand services that you consume as compute, storage, and networking. Here is a detailed step-by-step explanation:

Infrastructure virtualization. Cloud providers start by virtualizing their physical servers using hypervisors or container orchestration platforms. This splits large physical machines into many isolated virtual machines (VMs) or containers.
Resource pooling and abstraction. These VMs, storage devices, and network components are grouped into large resource pools. You no longer see individual servers. Instead, you see abstract units like vCPUs, GBs of RAM, storage volumes, and virtual networks.
Resource provisioning. Through a web portal, CLI, or API, you request compute instances, storage, or networking. The cloud control plane translates your requests into actions on the underlying infrastructure. The control plane provisions the underlying infrastructure by creating VMs, attaching disks, assigning IPs, and configuring security groups.
Workload deployment. Once instances and storage are ready, you deploy your applications (web servers, databases, microservices) onto them. Load balancers, DNS, and virtual networks are configured so that traffic routes correctly between components and out to end users.
Traffic distribution and performance management. Incoming requests are distributed across multiple instances using load balancers and autoscaling policies. As demand rises, new instances can be launched; as it falls, unnecessary instances are terminated.
Monitoring and automation. Metrics and logs (CPU, memory, latency, error rates) are continuously collected. Based on these signals, automated rules can restart failed instances, scale the environment, or trigger alerts. Backups, snapshots, and replication further protect data and enable quick recovery.
Usage tracking. Throughout this process, the cloud platform measures your consumption of compute time, storage capacity, and data transfer. Instead of paying for fixed hardware, you are billed for what you actually use.

Cloud hosting fundamentally shifts how organizations think about infrastructure. Instead of purchasing and operating hardware, teams focus on application logic and business outcomes while the cloud handles elasticity, reliability, and operational complexity behind the scenes. This model not only accelerates deployment cycles but also enables experimentation. Resources can be provisioned in minutes and retired just as quickly if they are no longer needed.

Key Features of Cloud Hosting

Cloud hosting comes with a set of core capabilities that distinguish it from traditional single-server hosting models. These features are built to handle dynamic workloads, improve resilience, and simplify infrastructure management:

Elastic scal a bility. Resources (CPU, RAM, storage) can be scaled up or down quickly based on load, often automatically through autoscaling policies. This prevents overprovisioning while maintaining stable performance during traffic spikes.
High availability and redundancy. Workloads run across multiple physical servers and often multiple availability zones. If one host fails, traffic is routed to healthy instances, reducing downtime and improving service continuity.
Resource pooling and multi-tenancy. Compute, storage, and network resources are pooled and shared across many customers, with logical isolation enforced by virtualization or containers. This maximizes hardware utilization and keeps tenant environments secure.
Pay-as-you-go pricing. Billing is typically based on actual usage⁠ (e.g., per vCPU-hour, GB of storage, and GB of data transfer). This model aligns costs with real demand and reduces upfront capital expenditure on hardware.
Programmable infrastructure (APIs and IaC). Most cloud platforms expose APIs for provisioning and managing resources. Combined with Infrastructure as Code (IaC) tools (e.g., Terraform, CloudFormation), this enables reproducible, automated deployments and environment consistency.
Global reach and regional deployments. Providers operate data centers in multiple geographic regions. You can place workloads closer to end users to reduce latency, meet data residency requirements, and implement geo-redundant architectures.
Integrated security and compliance tooling. Built-in features like network security groups, identity and access management (IAM), encryption services, and logging help enforce least privilege, protect data, and support compliance with industry standards.
Managed services ecosystem. Databases, queues, caches, object storage, analytics, and other services are available as fully managed offerings. Offloading these building blocks reduces operational overhead and accelerates application development.

Together, these features make cloud hosting a flexible, resilient, and automation-friendly foundation for modern applications and services.

Cloud Hosting Architecture

Cloud hosting architecture is typically organized into layered components that separate how infrastructure is managed from how applications run.

At the bottom is the physical layer, which includes servers, storage arrays, and networking gear in multiple data centers. The provider builds a virtual infrastructure layer over them using hypervisors and software-defined networking (SDN).

Above that sits the control plane. This layer exposes management consoles, CLIs, and APIs for provisioning and configuring resources. The data plane actually processes user traffic and application workloads.

Cloud environments are usually segmented into regions and availability zones. Each zone consists of one or more isolated data centers. This layout lets you design applications that survive localized failures by distributing components across zones and, when needed, across regions.

On top of the core infrastructure, cloud hosting adds service layers that further shape the architecture of deployed workloads. At the IaaS layer, you work directly with virtual machines, block and object storage, and virtual networks. At higher layers, managed databases, Kubernetes clusters, serverless functions, and message queues abstract away more of the operational complexity.

Multi-tenancy is enforced through strong isolation boundaries, such as VPCs, subnets, security groups, IAM policies, and sometimes dedicated hosts for stricter compliance needs. Observability is built into the architecture via centralized logging, metrics collection, and tracing services that integrate with both infrastructure and applications.

Together, these elements create a modular architecture, where each piece (compute, storage, networking, identity, and observability) can be composed and scaled independently to support diverse cloud-hosted workloads.

Cloud Hosting Workloads

Cloud hosting workloads range from simple websites to complex, distributed systems.

Typical workloads include web and application servers, APIs, and microservices that scale horizontally behind load balancers; databases and data warehouses that rely on high-performance storage and replication; and analytics, ETL, and big data pipelines that process large volumes of streaming or batch data. Cloud platforms also commonly host CI/CD pipelines, test environments, and ephemeral dev sandboxes that spin up and down automatically as part of the software delivery process.

More advanced workloads include containerized applications orchestrated by Kubernetes, event-driven serverless functions, AI/ML training and inference jobs, and IoT backends that ingest telemetry from millions of devices.

All these workloads benefit from the cloud’s ability to rapidly provision resources, isolate environments, and integrate with managed services for logging, monitoring, security, and messaging.

Types of Cloud Hosting

Types of cloud hosting describe how infrastructure is owned, isolated, and managed. Understanding cloud deployment models helps you choose the right balance of control, flexibility, cost, and compliance for your environment.

Public Cloud Hosting

Public cloud hosting runs your workloads on shared infrastructure owned and operated by a cloud provider. Compute, storage, and networking resources are logically isolated per tenant but physically shared with other customers. You provision virtual machines, containers, and managed services inside your own logically isolated environment (e.g., VPC). At the same time, the provider manages the underlying hardware, data centers, and core networking. Capacity is effectively elastic, and you can deploy resources in multiple regions. This helps you achieve global reach without owning any physical infrastructure.

The public cloud model is particularly attractive for variable or unpredictable workloads and greenfield applications. It supports rapid experimentation, DevOps practices, and modern architectures like microservices and serverless. The main trade-offs are reduced control over the physical layer, dependence on the provider’s platform and SLAs, and the need to design carefully for security, data sovereignty, and cost governance.

Private Cloud Hosting

Private cloud hosting provides cloud-like capabilities, such as self-service provisioning, virtualization, and automation on infrastructure dedicated to a single organization. This can be on-premises in your own data center or in a provider’s facility using dedicated hardware. In both cases, the data center does not share hardware resources with other customers. You still get abstraction, APIs, and orchestration, but with tighter control. This control relays to configurations, network topology, and integration with existing enterprise systems.

Private cloud is often suitable for organizations with strict compliance, data locality, or security requirements that make multi-tenant public cloud less suitable. It also helps large enterprises consolidate fragmented virtualized environments into a more standardized, automated platform. The trade-off is higher responsibility. Namely, you bear a greater operational burden and face higher fixed costs than in a purely public cloud environment.

Hybrid Cloud Hosting

Hybrid cloud hosting combines public and private cloud environments, allowing workloads and data to flow between them based on business or technical requirements. A typical pattern is to keep sensitive systems of record or latency-critical applications in a private cloud or on-premises, while using public cloud resources for burst capacity, analytics, or customer-facing services. Connectivity is via VPNs or dedicated links, and identity, networking, and security policies apply across environments.

The hybrid model aims to balance control and agility: you keep tight control over critical workloads while exploiting public cloud elasticity and services where appropriate. However, hybrid architecture adds complexity in areas such as network design, security policy enforcement, observability, and data replication. Successful hybrid deployments rely on consistent management and automation across both sides (for example, common tooling for IaC, CI/CD, monitoring, and access control) to avoid creating two disconnected operational silos.

Multi-Cloud Hosting

Multi-cloud hosting uses services from two or more public cloud providers simultaneously. This can be a deliberate strategy to avoid vendor lock-in, to leverage best-of-breed services from different providers (e.g., a specific AI platform or database), or to meet regulatory and residency requirements in different regions. A multi-cloud setup can spread an application across multiple clouds for redundancy, or different components may live in different providers based on cost and capabilities.

While multi-cloud can increase resilience and negotiation leverage, it introduces additional operational and architectural complexity. You must manage multiple sets of APIs, IAM models, networking constructs, and billing systems. To make multi-cloud manageable, organizations often introduce an abstraction layer, such as Kubernetes, service meshes, or multi-cloud management platforms, and standardize their practices around portable tooling (Terraform, cross-cloud monitoring, centralized identity). Even then, true workload portability requires discipline in avoiding provider-specific lock-in at the application layer.

Learn more about how to deploy and manage your multi-cloud environments with our comprehensive list of tool options.

Managed Cloud Hosting

Managed cloud hosting is a service model where a provider (or MSP) builds and operates your cloud environment on top of a public or private cloud platform. You still benefit from cloud characteristics, such as elasticity, pay-as-you-go resources, and global reach, but offload much of the day-to-day operational work. The managed provider typically handles architecture design, provisioning, patching, security hardening, backups, monitoring, incident response, and sometimes cost optimization, while you focus on applications and business logic.

This model is attractive for teams that lack in-house cloud expertise or don’t want to run a large operations function. It can accelerate cloud adoption, reduce misconfigurations, and provide access to specialized skills (e.g., Kubernetes operations, security engineering). The trade-offs include higher recurring service costs and some loss of direct control. Furthermore, changes often go through the managed provider’s processes and SLAs. Clear division of responsibilities, transparent monitoring, and well-defined runbooks are critical to making managed cloud hosting effective and predictable.

Managed Cloud Services

Managed cloud services are layers of operational support and automation built on top of cloud infrastructure. Instead of your team configuring and maintaining every component, a provider designs, runs, and optimizes parts of your stack so you can focus more on applications and business logic.

Managed Infrastructure (Compute, Storage, Network)

Managed infrastructure services handle provisioning, configuration, patching, and lifecycle management of virtual machines, storage volumes, and virtual networks. The provider designs the baseline architecture (VPCs, subnets, security groups, routing), sets up standardized images and templates, applies OS updates and hardening, and ensures capacity is available when you need it. You still control what runs on the instances, but the underlying infrastructure management should adhere to best practices and SLAs.

Managed Databases

Managed database services offload the operational burden of running relational or NoSQL databases. The provider handles installation, patching, backups, replication, automatic failover, point-in-time recovery, and often routine performance tuning. You interact with the database via endpoints and connection strings, define schema and queries, and tune indexes when needed, while the platform ensures high availability, encryption, monitoring, and scaling (vertical or horizontal, depending on the engine).

Managed Kubernetes and Containers

Managed Kubernetes (or managed container platforms) provide a fully operated control plane and standardized worker node lifecycle. The provider sets up and maintains clusters, upgrades Kubernetes versions, patches node OSes, integrates cluster logging and metrics, and exposes APIs for deploying containers. This lets teams focus on building containerized applications and defining manifests or Helm charts, rather than worrying about etcd health, control-plane HA, node provisioning, or CNI/CNI plugin management.

Managed Security Services

Managed security services cover continuous security configuration, monitoring, and incident response around your cloud workloads. Offerings can include managed firewalls, WAFs, intrusion detection/prevention, vulnerability scanning, SIEM/SOAR integration, and 24/7 security operations center (SOC) coverage. The provider tunes rules, triages alerts, responds to incidents according to runbooks, and supplies compliance-ready reports, while you remain responsible for application-level security and access policies.

Managed Backup and Disaster Recovery

Managed backup and DR services automate data protection and recovery workflows across your cloud environment. The provider designs retention policies, configures snapshot schedules, replicates data across regions or sites, and regularly test restore procedures and failover plans. In an outage or data loss event, they coordinate restoring systems to a known-good state or failing over to a secondary environment, reducing RPO and RTO without your team having to script and maintain all the backup logic.

Managed Monitoring and Observability

Managed monitoring and observability services centralize metrics, logs, and traces from your infrastructure and applications, and maintain the underlying tooling. The provider deploys agents, configures dashboards and alerts, manages data retention, and tunes thresholds to reduce noise. They may also provide SRE-style support: capacity recommendations, SLA/SLO tracking, and incident reviews. You consume the insights and dashboards, while the provider ensures that telemetry pipelines and observability platforms stay healthy and up to date.

Managed Identity and Access Management (IAM)

Managed IAM services focus on designing, implementing, and maintaining secure identity and access controls in the cloud. This includes user and role management, SSO integration, MFA enforcement, least-privilege role design, and periodic access reviews. The provider builds and maintains the IAM policies, maps them to your organizational structure, and helps respond to access-related incidents or audits, while you define who should have access to what from a business perspective.

Managed DevOps and CI/CD

Managed DevOps services build and operate your CI/CD pipelines and supporting tooling (source control integrations, artifact repositories, runners, and deployment workflows). The provider sets up standardized pipelines for build, test, security scanning, and deployment; maintains the underlying runners and agents; and enforces release processes and approvals. This lets engineering teams ship changes quickly and reliably without dedicating significant internal resources to pipeline plumbing and infrastructure.

Cloud Hosting Benefits

Cloud hosting offers a combination of flexibility, automation, and resilience that’s difficult to achieve with traditional single-server setups. The main benefits revolve around how cloud computing resources are provisioned, scaled, secured, and paid for, and they include:

Elastic scalability. CPU, RAM, and storage can be quickly scaled up or down based on load, often automatically via autoscaling policies, so you can handle traffic spikes without overprovisioning.
High availability and fault tolerance. Workloads are distributed across multiple servers and availability zones, reducing single points of failure and improving uptime when hardware or components fail.
Performance optimization. Different instance types, storage tiers, and networking options (e.g., SSD-backed volumes, low-latency networks, edge locations) help you align performance with specific workload requirements.
Cost efficiency and OpEx model. Large upfront hardware purchases are replaced with pay-as-you-go billing, so you pay only for the compute, storage, and bandwidth you actually consume.
Faster provisioning and time-to-market. Environments are spun up in minutes via portals, APIs, or IaC templates, rather than waiting for hardware procurement, racking, and manual configuration.
Operational offload. The undifferentiated heavy lifting, hardware maintenance, power, cooling, base networking, and often managed services like databases or Kubernetes are offloaded to the provider.
Global reach and lower latency. Workloads are deployed in multiple geographic regions and edge locations to serve users closer to where they are, reducing latency and improving user experience.
Built-in security and compliance tooling. Integrated IAM, encryption, network segmentation, logging, and compliance enable least-privilege access, protect data, and support regulatory requirements.

Overall, these benefits make cloud hosting a flexible, resilient, and cost-effective foundation for running modern digital services at any scale.

Cloud Hosting Challenges

Cloud hosting also introduces complexities and risks that organizations must account for when designing and operating workloads in the cloud. These challenges typically stem from shared responsibility, distributed systems design, and rapidly changing cloud ecosystems:

Cost management and sprawl. Usage-based billing can lead to unexpected expenses if resources aren’t monitored and governed. Idle instances, overprovisioning, and inefficient architectures can quickly inflate monthly costs.
Security and shared responsibility. While providers secure the infrastructure, customers must secure access controls, data, and workload configurations. Misconfigured IAM, open storage buckets, or weak network rules remain common risks.
Increased operational complexity. Distributed cloud environments require robust automation, monitoring, and incident response. Teams must adopt new operational models (DevOps/SRE) and ensure observability across many moving parts.
Vendor lock-in and portability limitations. Relying heavily on proprietary services can make it difficult to switch providers. Also, it is challenging to run workloads elsewhere without major refactoring or migration efforts.
Compliance and data residency. Organizations in regulated industries must ensure workloads meet jurisdictional requirements. They must maintain audit trails and control where sensitive data is stored and processed.
Skill gaps and training requirements. Cloud-native architectures demand specialized knowledge of Kubernetes, automation, and security tooling. Many teams must learn it or hire for it, which increases onboarding and transformation timelines.

Organizations can mitigate risks while still capturing the full value of cloud hosting by understanding these challenges early and planning accordingly.

Cloud vs. Other Types of Hosting Solutions

Choosing the right hosting model depends on how much control you need, how your workloads scale, and how quickly your infrastructure must adapt to change. Comparing cloud hosting with traditional alternatives helps clarify where cloud delivers the most value and where other solutions may still be a better fit.

Cloud vs. Traditional Web Hosting

Traditional web hosting differs from cloud hosting primarily in how it provisions, isolates, and scales resources. In traditional hosting models, such as shared, VPS, or single dedicated servers, you’re tied to a fixed amount of CPU, RAM, and storage on a single physical machine (or a small cluster), and scaling usually requires manual upgrades or migrations.

Cloud hosting, by contrast, runs workloads on virtualized infrastructure across many physical servers. It exposes pooled resources via APIs and allows near-instant scaling up or down based on demand. High availability in traditional hosting often requires custom failover setups. Meanwhile, cloud platforms natively support multi-AZ deployments, load balancing, and automated recovery. Operationally, traditional hosting is simpler but less flexible, while cloud hosting introduces more architectural complexity.

Cloud vs. Shared Hosting

Cloud hosting and shared hosting mainly differ in isolation, scalability, and reliability. In shared hosting, many customers’ websites run on the same physical server and OS instance, sharing CPU, RAM, disk, and IP. If one tenant spikes resource usage or gets compromised, others can be affected. Performance tuning and scaling options are limited, and you typically get a fixed plan with minimal control over the environment.

In cloud hosting, workloads run on virtualized resources with stronger isolation, and you can scale horizontally or vertically, often automatically, based on demand. You also benefit from higher availability through redundant infrastructure, richer networking and security controls, and pay-as-you-go pricing, at the cost of more configuration responsibility and a steeper learning curve than simple shared hosting plans.

Cloud vs. VPS

Cloud hosting and VPS (Virtual Private Server) hosting both use virtualization, but they differ significantly in architecture, scalability, and resilience. A VPS typically runs on a single physical server where multiple virtual machines share resources (CPU, RAM, storage); if that host fails, it affects all VPS instances on it.

Cloud hosting, by contrast, draws from a cluster of servers and storage, so you can distribute workloads across multiple hosts with built-in redundancy and autoscaling. This makes the cloud better suited for variable or high-growth workloads that need rapid scaling, multi-zone deployments, and integration with managed services.

VPS hosting usually offers more predictable, fixed monthly pricing and can be a good fit for smaller, steady workloads that don’t require advanced automation or high availability, but it lacks the elasticity and fault tolerance inherent to cloud-native platforms.

Cloud vs. Dedicated Server

Cloud hosting runs workloads on virtualized resources spread across many physical servers. This gives you elastic scalability, pay-as-you-go pricing, and access to a wide range of managed services. However, you have less direct control over the underlying hardware. A dedicated server gives you exclusive use of a single physical machine with fixed CPU, RAM, and storage, offering predictable performance, strong isolation, and full control over OS and hardware configuration. However, scaling usually means provisioning additional servers manually and committing to longer-term contracts.

In practice, the cloud is better for variable or rapidly growing workloads that benefit from automation and on-demand capacity. Dedicated servers suit stable, performance-sensitive, or licensing-constrained workloads that require consistent resources and low-level tuning.

Cloud Hosting Security

Cloud hosting security includes a shared responsibility model. This model entails the provider securing the underlying infrastructure, while you secure your workloads, identities, and data. Providers harden data centers, manage physical access, and protect core services like compute, storage, and networking with isolation mechanisms (hypervisors, VPCs, security groups) and continuous patching. They also supply native tools for encryption at rest and in transit, identity and access management (IAM), key management services (KMS), and logging, giving you a robust defense-in-depth strategy.

On your side, strong security depends on how you configure and use these building blocks. This includes enforcing least-privilege access through well-designed IAM roles, enabling MFA and SSO, segmenting networks with subnets and firewalls, and encrypting sensitive data with properly managed keys. You also need end-to-end observability, including centralized logs, metrics, and alerts to detect misconfigurations, suspicious behavior, and policy violations in real time. Regular security reviews, automated compliance checks, and infrastructure-as-code scanning help prevent drift and configuration errors, which are among the most common causes of cloud security risks.

Who Should Use Cloud Hosting Solutions?

Cloud hosting solutions are a strong fit for organizations that need to scale quickly, iterate fast, and avoid the overhead of owning and operating physical infrastructure, such as SaaS providers, ecommerce platforms, digital agencies, startups, and enterprises modernizing legacy systems. Teams building microservices, CI/CD-driven environments, or data-intensive analytics pipelines benefit from on-demand capacity, managed services, and global reach.

At the same time, workloads that require consistent high performance, strict isolation, or specific licensing (for example large databases, high-traffic APIs, or latency-sensitive backend services) can combine classic cloud hosting with bare metal cloud. This lets you mix virtualized instances and bare metal in one architecture, using each where it makes the most technical and economic sense.

Migrating to Cloud Hosting

Migrating to cloud hosting starts with a clear assessment of your existing workloads. This includes applications, databases, dependencies, and network flows. The assessment helps you decide which components to lift-and-shift, which to replatform, and which to refactor for cloud-native architectures.

From there, you design target environments (VPCs, subnets, security groups, IAM roles). Then, you can choose appropriate services (VMs, containers, managed databases), and plan data migration strategies. Aim to minimize downtime, such as replication, phased cutovers, or blue-green deployments.

Successful migrations also align backup, monitoring, and incident response with cloud tooling, update security and compliance controls to the shared responsibility model, and introduce cost governance (tagging, budgets, rightsizing). Finally, running pilot migrations, load tests, and rollback drills before full cutover helps validate performance and reliability, reducing the risk of surprises in production.

Learn more about data center migration and database migration.

Cloud Hosting Trends

A prominent cloud hosting trend is mass hybrid and multi-cloud strategy adoption. These strategies combine public cloud, private cloud, and edge resources to optimize performance and improve resilience.

At the same time, the integration of AI/ML services into cloud platforms is increasing. Cloud providers are offering specialized infrastructure for training and serving models. This makes it easier for organizations to build data-driven and intelligent applications without owning expensive hardware.

Edge computing, which includes blending cloud with geographically distributed edge nodes. is growing in importance. This is evident in workloads that require low latency or real-time processing, such as IoT, AR/VR, and real-time analytics. Meanwhile, as cloud usage balloons, so does cost pressure. This is driving demand for better cost governance practices (often called “FinOps”). Subsequently, automated resource management and sustainable cloud operations balance performance with environmental and economic efficiency.

The Road Ahead for Cloud-Powered Infrastructure

Cloud hosting has become a foundational approach for delivering scalable, resilient, and globally accessible digital services. By leveraging virtualized infrastructure and automation, organizations can focus on innovation and delivering value rather than maintaining hardware.

While cloud architecture comes with shared responsibility and operational complexity, the benefits still make it the preferred model. As trends like hybrid cloud and AI-driven operations reshape the landscape, cloud hosting remains central to businesses.

Data Center Migration: Benefits, Checklist, Best Practices

Data center migration is the process of transferring an organization’s digital assets, such as servers, storage systems, applications, and network infrastructure from one environment to another.

Whether prompted by hardware aging, cloud adoption, consolidation, or compliance demands, such projects require meticulous planning to avoid data loss, downtime, and service disruption. When properly executed, migration enhances scalability, efficiency, and resilience while preserving business continuity.

This article explains data center migration, its types, and benefits, and answers the most common questions about its implementation.

What Is Data Center Migration?

Data center migration involves relocating compute, storage, networking, and security workloads from one hosting environment to another between on-premises facilities, colocation sites, private clouds, public clouds, or hybrid combinations. The process covers both physical moves and logical transitions: servers may be rehosted as virtual machines or containers, data sets replicated or re-sequenced, and networks re-addressed or extended to preserve connectivity and policies. Migration reshapes not only where systems reside but also how they are provisioned, monitored, secured, and cost-managed.

Successful migration requires structured execution. It begins with discovery and dependency mapping to identify interconnections, followed by target-state design that accounts for performance, capacity, latency, and security. Data synchronization and controlled cutover procedures ensure integrity and minimize downtime. Governance, testing, rollback plans, and post-move optimization form the foundation for maintaining service levels, compliance, and operational stability.

To learn more about maintaining optimal operational levels in your data center, check out our article on data center energy efficiency.

Types of Data Center Migration

Data center migrations can take several forms depending on the starting environment, target architecture, and strategic goals. Each type presents its own technical challenges and considerations for data integrity, downtime tolerance, and operational continuity. Understanding the key differences helps organizations select the right migration model and tooling for their workloads.

Physical-to-Physical (P2P) Migration

A physical-to-physical migration involves moving servers, storage, and networking equipment from one data center facility to another. This often happens when an organization relocates to a new site, upgrades to a modernized facility, or consolidates multiple sites into a single, more efficient one. P2P migrations require precise logistics, such as planning rack layouts, power and cooling capacities, cabling, and timing to ensure that critical systems are shut down, transported, and reactivated with minimal service interruption.

The process demands careful dependency mapping, as even brief downtime in interconnected systems can cascade through applications. Teams often replicate or back up data before the move and perform a phased transition to avoid full outages.

While this migration preserves existing hardware configurations, it rarely improves scalability or automation capabilities. Its main advantage is control. Namely, organizations maintain ownership of their physical infrastructure while modernizing their facilities.

Physical-to-Virtual (P2V) Migration

Physical-to-virtual migration replaces legacy hardware with virtualized infrastructure, converting operating systems, applications, and data into virtual machines. It allows multiple workloads to run on shared hypervisors, reducing physical footprint and increasing flexibility. This approach typically uses P2V conversion tools to capture disk images, drivers, and configurations from physical hosts and replicate them in a virtual environment such as VMware vSphere, Hyper-V, or KVM.

The main goal of a P2V migration is to optimize resource utilization and simplify management. It also enables features like snapshots, high availability, and live migration that are impossible on bare metal. However, P2V projects require validation to ensure that hardware-dependent applications function correctly in a virtualized environment. Performance tuning, driver compatibility, and software licensing must all be addressed before decommissioning the original hardware.

Virtual-to-Virtual (V2V) Migration

A virtual-to-virtual migration transfers workloads between virtualization platforms or hypervisors, such as moving from VMware to KVM or from on-premises hypervisors to a cloud-based virtualization layer. This process is common when organizations adopt open-source alternatives, transition to managed services, or align infrastructure with vendor ecosystems. It also occurs during mergers and acquisitions, when different virtualization technologies must be unified.

V2V migration focuses on maintaining application performance and compatibility across differing hypervisor architectures. Conversion utilities are used to reformat disk images, adjust virtual hardware definitions, and update guest OS drivers. Although less disruptive than hardware moves, V2V transitions require rigorous testing to validate bootability, network connectivity, and system dependencies in the new environment.

Virtual-to-Cloud (V2C) or Cloud Migration

Virtual-to-cloud migration involves relocating virtual machines, applications, and data to a public or private cloud environment. This approach leverages cloud-native benefits such as scalability, on-demand resources, and global reach. Workloads may be “lifted and shifted” as-is, rehosted using migration services, or refactored into containerized or serverless architectures. The goal is to reduce capital expenditure, improve agility, and access advanced services for analytics, AI, and automation.

Cloud migrations introduce new variables such as latency, security posture, compliance requirements, and cost management. Network architecture, identity federation, and observability must be redesigned for distributed operations. A successful V2C migration relies on hybrid connectivity, encrypted data transfer, and a well-defined rollback plan to maintain business continuity throughout the transition.

Cloud-to-Cloud (C2C) Migration

Cloud-to-cloud migration transfers workloads and data between cloud providers or between regions within the same provider. Organizations typically pursue C2C migrations to optimize costs, comply with data residency laws, or improve performance through multi-cloud or edge strategies. The process may include migrating VMs, storage buckets, databases, and APIs, while reconfiguring authentication, networking, and monitoring.

Because cloud platforms differ in architecture and services, C2C migration often involves refactoring workloads and reconfiguring dependencies to maintain compatibility. Data transfer methods such as replication, snapshot export, or cross-cloud pipelines are used to minimize downtime. Proper planning ensures that service-level agreements remain intact and that operational metrics, such as latency and throughput, meet post-migration benchmarks.

Optimization of data center migration starts with timely and accurate planning. Read more about it in our article on data center capacity planning.

Reasons for Data Center Migration

Organizations migrate data centers to reduce risk, improve performance, and align infrastructure with business goals. Here are the main reasons for data center migration:

Hardware lifecycle. Aging infrastructure loses vendor support, increasing failure and security risk.
Facility constraints. Limited power, cooling, or space restricts expansion or modern hardware deployment.
Cost efficiency. Consolidating resources or moving to cloud-based OpEx models lowers operational costs.
Performance optimization. Relocating workloads closer to users or data sources reduces latency.
Resilience and compliance. Multi-site redundancy and modern security controls strengthen reliability and meet regulatory mandates.
Scalability and modernization. Cloud and virtualization enable automation, elasticity, and simplified management.

Data Center Migration Steps and Best Practices

A successful migration follows a structured sequence that balances precision with operational safety. Each step pairs with a proven best practice:

Discovery and assessment - Document and validate dependencies early. Catalog all assets, dependencies, and data flows using automated discovery tools to eliminate blind spots.
Strategy and planning - Align goals with business priorities. Define scope, budget, and success metrics. Match migration methods (rehost, replatform, refactor) to organizational objectives.
Design of target environment - Build for scalability and resilience. Architect the new data center or cloud environment with redundancy, automation, and segmentation for high availability.
Data and application preparation - Ensure consistency and compatibility. Back up, cleanse, and test data and applications. Validate OS and version compatibility before cutover.
Migration execution - Minimize downtime through phased cutover. Start with non-critical workloads, use replication for incremental syncs, and maintain rollback readiness.
Validation and testing - Verify functionality before decommissioning. Test performance, security, and connectivity using baseline metrics and automated health checks.
Optimization and documentation - Refine configurations post-move. Fine-tune for cost and efficiency, update runbooks, and record lessons learned for future migrations.

By following these steps and applying best practices consistently, organizations minimize disruption, safeguard data integrity, and ensure a smooth, predictable transition to their new environment.

Data Center Migration Checklist

A well-structured checklist helps keep a data center migration organized, reduces risks, and ensures nothing is overlooked during the move. The steps below cover the essential actions, from planning to post-migration verification:

Define project goals and scope. Identify what needs to be moved, why the migration is happening, and what success looks like.
Assemble the migration team. Assign roles for project management, networking, storage, applications, and testing.
Create a complete asset inventory. List all servers, applications, databases, and dependencies that will be affected.
Assess risks and plan for downtime. Estimate impact on operations and prepare rollback or recovery options.
Design the target environment. Plan the new data center layout or cloud architecture with proper capacity, networking, and security.
Back up all data. Ensure complete and verified backups exist before any migration begins.
Test connectivity and compatibility. Check network routes, system dependencies, and software versions in the new environment.
Plan and communicate the migration schedule. Inform stakeholders of maintenance windows, expected outages, and milestones.
Run a pilot migration. Test a small subset of workloads to validate tools, timing, and procedures.
Perform the migration. Move data and workloads according to the plan, monitoring for errors or delays.
Verify systems after cutover. Confirm all applications, services, and data are operational and intact.
Monitor and optimize. Track performance in the new environment and adjust resources as needed.
Document results and lessons learned. Record what worked, what didn’t, and recommendations for future migrations.

A well-structured checklist not only streamlines the migration process but also serves as a safeguard. It helps teams stay organized, minimize errors, and ensure a smooth transition from planning to post-migration optimization.

Data Center Migration Tools

Data center migration tools streamline discovery, replication, and orchestration while safeguarding performance and compliance. Key categories include:

Asset discovery and dependency mapping. Automate inventory and relationship mapping (e.g., ServiceNow Discovery, Device42, Dynatrace).
Migration orchestration. Manage replication, cutover, and rollback (e.g., VMware HCX, AWS MGN, Azure Migrate, Zerto).
Data replication and backup. Enable low-downtime transitions (e.g., Veeam, Commvault, Rubrik, NetApp SnapMirror).
Database migration and CDC. Keep data synchronized across environments (e.g., Oracle GoldenGate, AWS DMS, Debezium).
Configuration management and IaC. Standardize target builds (e.g., Terraform, Ansible, Puppet).
Observability and testing. Validate system health and performance (e.g., Datadog, Grafana, k6, Postman).
Security and compliance tools. Verify IAM, encryption, and posture (e.g., Prisma, Wiz, Qualys, OpenSCAP).

Together, these tools form an integrated ecosystem that streamlines every phase of migration, from initial discovery and replication to cutover validation. It ensures data integrity, operational continuity, and faster time to stabilization.

Data Center Migration Challenges

Even well-planned migrations face risks that can derail timelines, inflate costs, or impact availability. The challenges below are the most common and the ones worth managing explicitly from day one:

Incomplete dependency visibility. Hidden app-to-app or app-to-database calls cause surprise outages or degraded performance after cutover.
Data integrity and sync gaps. Long transfer windows and change churn risk drift between source and target, leading to corruption or stale data.
Downtime and rollback complexity. Tight maintenance windows and interlocked systems make coordinated cutover and a clean failback hard to execute.
Performance regressions. Differences in storage latency, network paths, or hypervisor settings can slow critical workloads despite “like-for-like” sizing.
Security and compliance drift. Recreated environments lack controls (IAM, segmentation, logging, encryption), exposing gaps in compliance with policies and audits.
Cost surprises. Underestimated egress, replication, or oversized target resources drive spend above plan, especially in hybrid or multi-cloud moves.

By anticipating these challenges early and addressing them through careful planning, automation, and thorough testing, organizations can significantly reduce migration risks and ensure a smooth, predictable transition to their new environment.

Data Center Migration FAQ

Here are the answers to the most commonly asked questions about data center migration.

How Long Does Data Center Migration Take?

Timelines vary widely with scope and method: a small rehost of a few VMs can be completed in weeks, while multi-site programs with hundreds of apps typically take 6–18 months. Duration depends on discovery quality, data volume and change rate (TB/PB and CDC needs), network latency and bandwidth, maintenance-window constraints, compliance/security work, and whether you rehost, replatform, or refactor.

Typical phases, such as assessment and planning, target build-out, replication/pilots, wave-based cutovers, and stabilization often overlap to compress time, but tight RTO/RPO targets, complex interdependencies, and vendor/lease lead times can lengthen schedules. Using bulk-transfer devices for initial loads, automating environment build with IaC, and migrating in waves during predefined windows helps keep the calendar under control.

Learn more about how to achieve data center compliance in our blog article.

What Is “Lift and Shift” Data Center Migration?

“Lift and shift” is a rehosting approach where you move applications and data to a new environment, often from on-prem or colo to cloud or a new virtual platform without changing the app’s code or architecture. You replicate disks and configurations, stand up equivalent compute, storage, and network constructs, update DNS and integrations, and cut over once data is synchronized. Tooling typically includes block-level replication, VM conversion, and orchestration services, aiming to preserve behavior and minimize refactoring risk.

How Do You Minimize Downtime During Data Center Migration?

Use continuous replication (block- or CDC-based) to keep target data nearly in sync, rehearse cutovers in a pilot, and migrate in waves, starting with low-risk systems. Schedule a short freeze window to quiesce writes, take a final incremental sync, and switch DNS/routing with low TTLs for quick propagation. Keep runbooks versioned, pre-stage infrastructure with IaC, and validate health checks immediately after cutover. Always have a tested failback plan so you can revert fast if KPIs or user journeys regress.

Moving Infrastructure Without Downtime

Data center migration is a complex but essential step in modernizing IT operations. Whether driven by aging infrastructure, regulatory pressure, or the need for scalability, a well-executed migration strengthens performance, resilience, and long-term agility. Success depends on disciplined planning, detailed discovery, and the use of automation to reduce human error and downtime. By combining the right tools, well-tested processes, and clear communication between teams, organizations can move workloads securely and efficiently, transforming their infrastructure into a more flexible foundation for future growth.

Data Center Selection: Key Requirements of a Data Center

Managing an in-house data center requires time, IT expertise, and a high budget, which is why businesses typically prefer to run operations from a third-party facility. While colocation may be a natural choice for many companies, choosing the right provider is not simple.

This article is a guide on how a business should approach data center selection. We outline the key factors to consider when choosing a colocation facility and offer guidance on finding a partner that meets your business needs.

12 Key Considerations When Choosing a Data Center

There are many benefits to setting up your equipment in a colocation facility:

Decreased capital costs when compared to an in-house data center.
Simpler management with little to no regular maintenance.
Greater reliability with less chance of downtime.
High levels of flexibility and scalability.
An opportunity to focus on avenues other than running a data center, such as IT improvements and innovation.

Despite these advantages, the benefits of colocation depend on your ability to assess business needs and select the right partner. The data center selection factors below will help you assess your options and make an informed decision.

Our article on colocation hosting offers an in-depth look at how this hosting type works and what benefits it offers when compared to an in-house data center.

Your Goals and Needs

Clarify your goals and objectives by partnering with a third-party data center. Consult every relevant team in your company, as different departments have varying needs and expectations. Discuss data center needs with your:

Upper management.
Network admins.
SecOps and DevOps teams.
Software developers.

Advising with different stakeholders provides a complete picture of your data center requirements. For instance, upper management may not know about problems technicians face, such as insufficient monitoring or alerting. On the other hand, technicians may not be privy to some long-term business objectives.

Once you consult with different stakeholders, prioritize the requirements, and create a list of long-term, high-level goals of partnering with a data center, such as:

Enhancing data center uptime.
Improving app performance.
Reducing the time the team spends on managing the IT setup.
Enhancing overall cybersecurity.

Once you know your goals, consider the size of your budget. Decide how much you can spend on colocation services and how long a contract term you can handle.

Facility Location

Facility location is vital to the reliability and safety of your IT equipment. Here are the primary location-related considerations you need to account for when searching for a data center:

The distance between your organization and the data center impacts Internet speed. This problem is not as notable with fiber wiring as these cables do not experience issues until the 25-mile mark. However, setting up in a facility a few states away from your customer base can cause problems.
Ideally, the data center should be easily accessible to your IT staff. Trips to a distant location can be a problem for regular maintenance and emergencies, plus you will need to cover the trip expenses.
You may need a data center close to the source of your data generation to avoid latency issues.
Find a data center that does not run on the same power grid as your business. That way, a regional power outage will not affect both the data and your company's office.
Look for a data center in an area that is not prone to natural disasters (tornados, earthquakes, floods, tsunamis, etc.).

Although you could save money by signing up with a center that is further away, setting up your equipment in a nearby facility is typically a better business move.

Learn about edge computing, a tech that brings data processing closer to the network's edge and removes the need for nearby data centers.

Data Center Infrastructure

The infrastructure includes physical and hardware-based resources that comprise a data center, including all IT devices, equipment, and technologies. Here are several key infrastructure factors when choosing a provider:

The facility needs top MEP (Mechanical, Electrical, Plumbing) systems. The MEP includes electrical systems, cooling mechanisms, proper ventilation, fiber optic cabling, air handling systems, etc.
A data center should have Uninterruptable Power Supplies (UPS) to protect client's setups from power failures, surges, and fluctuations.
The data center should have high PUE (Power Usage Effectiveness). This metric is a ratio that describes how efficiently a facility uses power, which impacts your monthly bill and the overall stability of the facility.
Consider the building's proximity to utilities like electrical grids and natural gas services (if the center has bi-fuel generators).
The facility should have connections to multiple power grids to ensure redundancy. In the event of a power shortage that takes out all grids, the data center should have batteries or flywheels.
The center needs a proper fire suppression system and environmental controls.

Be cautious when data centers operate on outdated tech. As no governing body forces businesses to keep their tech up to date, make sure you do not end up paying 2020s prices for 2000s tech.

Service Reliability

Keeping your business up and running is a vital aspect of a data center. All providers have SLAs (Service Level Agreements) that provide assurances in terms of:

Network uptime.
Power service.
Temperature stability.

Data centers measure reliability in terms of guaranteed uptime, a metric outlined within the SLA. All data centers have a rating based on how many redundant systems the facility has. This classification system works on a scale of 1 to 4:

Tier I: Basic site infrastructure that offers little to no redundancy. These facilities guarantee 99.671% uptime (about 29 hours of downtime per year).
Tier II: These facilities have partial redundancy in the cooling and electrical systems. As a result, there is 99.749% uptime (about 22 hours of downtime per year).
Tier III: These facilities have concurrently maintainable site infrastructure and have a 99.982% uptime rating (1.6 hours of downtime annually).
Tier IV: These are the "4 nines" facilities with 2N+1 redundancy across all infrastructure (completely paralleled backup systems).

When choosing a specific tier, remember that a Tier IV is not always the best option. While the lack of downtime is excellent, a Tier III data center selection provides enough uptime and allows you to spend the difference in cost on improving your company.

Examine the SLA in detail before you side with a data center. Besides basic uptime guarantees, you should also check for bandwidth limits and burst costs. Your data center should also have disaster recovery plans in place for natural emergencies, mishaps, power failures, acts of terror, etc.

Our Disaster-Recovery-as-a-Service (DRaaS) offering grants the ability to quickly recover from incidents and ensure business continuity in all scenarios.

Data Center Security

A single data breach can cripple a business, so a center requires robust security to keep client setups and info safe. In terms of physical security, a data center should have:

Gated grounds.
Perimeter security with 24/7 guard posts.
Access controls with locked doors and racks.
Video monitoring of each rack row.
Closed-circuit security monitoring.
Live technical surveillance by expert NOC staff.
Mantraps with multi-factor authentication.
Windowless environments.

Your data center must also have a comprehensive suite of cyber security solutions that include:

Firewalls that prevent a wide range of attacks.
Robust network security with a strong intrusion detection system.
DDoS mitigation capabilities.
Data backups that allow users to restore data quickly and easily in case of an attack.
Advanced monitoring systems.
Regular vulnerability scanning that helps keep the system up to date and safe from cyber threats.

Assessing a center's levels of security is challenging as all sales teams will boost about high levels of protection. Good tactics to realistically gauge the safety of a data center is to:

Pay them an in-person visit to see the physical security measures first-hand.
Inquire about the process for getting a vendor on-site to replace parts.
Ask about SSAE 16 and SOC certification.
Ask about the facility's PCI DSS and HIPAA compliance.

High cyber and physical security levels are a notable benefit of setting up equipment at a third-party facility. You would have to invest heavily in your IT setup and capable staff to reach the same levels of monitoring and protection as in a colocation center.

Continue learning about how careful business owners keep their facilities safe by checking out our article on data center security.

Levels of Scalability

If your company has varying requirements, finding a data center that can keep up with your demands is vital. If your business doubles in size or you decide to take on additional projects, your data center partner needs to allow you to scale the operation.

The main questions to answer when considering scalability are:

What do you need to deploy now, and how will that change over time (storage capacity, rack space, bandwidth, power, connectivity, etc.)?
Is the facility operating at capacity, or does it have room for expansion?
How quickly can the provider scale your setup to meet the current requirements?

Your partner should also allow you to scale down in certain circumstances to optimize usage and control costs.

Do you know that IT advancements are so rapidly pushing the average rack density up that it is disrupting traditional practices in data centers? Learn more about it!

Carrier Neutrality

Your data center should be carrier-neutral. Neutrality gives a high degree of agility as the facility can switch between providers if one has an issue. Look for a data center with a wide range of:

Network providers.
Internet exchange (IX) facilities.
Cloud providers.

Access to different networks and operators is vital. A variety of cloud options and connectivity enable you to set up hybrid and mult i -cloud infrastructures without risking vendor lock-in.

On-Hand Support

Different data centers offer varying degrees of support. You may need help with the setup or migration of your equipment. Once set up, your IT infrastructure will require monitoring and maintenance as your in-house staff will not be on-site to manage all alerts and events.

The primary data center selection factors in terms of support are:

How the data center helps with the initial installation.
The level of post-setup help.
The monitoring tools the center's team uses.
If the support is available 24/7/365.
The technical qualifications of on-site engineers.
Remote monitoring capabilities and reporting.
If the facility allows a single view across all your deployments.

Our guide to data center migration outlines the best practices that enable you to organize an error-free move to a new facility.

Data Center's Reputation

Like with any other investment, you should do your research and examine the provider's reputation when making a data center selection. You can:

Read testimonials from clients.
Search online for client feedback.
Reach out to current or ex-clients for direct impressions.
Read available press releases or bulletins.
Inquire about the company's financial stability.

While no data center will have a perfect record, examining the provider's reputation gives insight into how the center handles issues. You will spot red flags early on and narrow the list of potential providers.

Costs and Pricing

You need to understand what you are paying for and why. On average, a monthly colocation fee ranges between $45 and $300 per unit. The set amount of bandwidth and IP addresses dictate the price, but operational costs (power, cooling, etc.) can also rack up the expenses.

Keep in mind that colocation services come with higher upfront costs than a cloud computing hosting solution. You need to invest in hardware instead of just migrating data to the provider's cloud and relying on VMs.

Our guide to colocation pricing ensures you understand your monthly bills and know how to estimate data center expenses accurately.

Avoiding Natural and Man-Made Disasters

A data center that is not prone to natural or human-caused incidents is vital to keeping your setup safe. Here are a few considerations:

Look for a facility dedicated to being a data center. Setting up equipment in a building with other businesses introduces unnecessary risks (kitchen fires, flooding from another office, lower security bars, etc.).
Check how prone the location is to natural disasters like floods or earthquakes.
Be cautious about data centers near airports, train lines, or highways. An accident on a road, rail line, or landing zone can put the facility out of commission.
Ensure the data center is not operating in a conflict-prone zone.
Check the shape of the facility's roof if possible. Leaks can be devastating, while roof repairs are messy and disruptive.

Also, investigate if the data center is planning any major expansions. While a growing center is not a concern, construction can cause power outages, rack damages, and tons of dust that can harm your equipment.

Extra Amenities

How big a factor are amenities in your data center selection? While typically not the main concern, an extra commodity or two can push you towards a particular provider. Some data centers offer:

On-site workstations for clients.
Access to fax machines, conference rooms, and offices.
Storages for your equipment.
Tech lounges.
On-site parking.

While other factors may be more important, a data center with the right mix of amenities can lead to a more comfortable and productive colocation experience.

Do Your Research and Make the Correct Data Center Selection

Choosing the wrong data center can lead to issues with connectivity, limited scaling, security breaches, and a ton of headaches. Evaluating provider reliability, network performance, and compliance standards can help prevent these problems.

Use the factors we listed above to narrow the list of potential providers and find a colocation partner that meets all your IT and business requirements. Making an informed choice ensures long-term operational efficiency and scalability.

IoT Edge Computing: What It Is and How It Works

In a classic IoT architecture, smart devices send collected data to the cloud or a remote data center for analysis. High amounts of data traveling from and to a device can cause bottlenecks that make this approach ineffective in any latency-sensitive use case.

IoT edge computing solves this issue by bringing data processing closer to IoT devices. This strategy shortens the data route and enables the system to perform near-instant on-site data analysis.

This article is an intro to IoT edge computing and the benefits of taking action on data as close to its source as possible. Read on to learn why edge computing is a critical enabler for IoT use cases in which the system must capture and analyze massive amounts of data in real-time.

What Is IoT Edge Computing?

IoT edge computing is the practice of using data processing at the network's edge to speed up the performance of an IoT system. Instead of sending data to a remote server, edge computing enables a smart device to process raw IoT data at a close-by edge server.

Data processing close to or at the point of origin results in zero latency. This feature can make or break the functionality of an IoT device that runs time-sensitive tasks.

Moving data processing physically closer to IoT devices offers a line of benefits to enterprise IT, such as:

Faster, more reliable services.
A smoother customer experience.
The ability to filter and aggregate raw data to reduce traffic sent to an external server or the cloud.
Real-time, on-site analytics.
Lower operational costs (OpEx) due to less bandwidth usage and smaller data center capacity needs.
Higher security due to fewer external connections and less room for potential lateral movement.

IoT edge computing is a vital enabler for IoT as this strategy allows you to run a low-latency app on an IoT device reliably. Edge processing is an ideal option for any IoT use case that:

Requires real-time decision-making.
Deals with extreme amounts of data.
Has potentially catastrophic failures.
Runs in an environment where cloud connectivity is either spare or entirely unavailable.

Cloud and edge computing are not mutually exclusive. The two computing paradigms are an excellent fit as an edge server (either in the same region or on the same premises) can handle time-sensitive tasks while sending filtered data to the cloud for further, more time-consuming analysis.

Edge Devices vs. IoT Devices

IoT edge computing relies on the combined use of both edge and IoT devices:

An IoT device is a machine connected to the Internet that can generate and transmit data to the processing unit (either an edge device, the cloud, or a central server). These devices usually have special-purpose sensors and serve a single purpose.
An edge device is a piece of hardware that operates near the user or device that generates raw data. These devices have enough computing resources to process data and make decisions with sub-millisecond latencies, a speed impossible to reach if data must first cross a network.

In some cases, the terms edge and IoT devices can be interchangeable. An IoT device can also be an edge device if it has enough compute resources to make low-latency decisions and process data. Also, an edge device can be a part of IoT if it has a sensor that generates raw data.

However, creating devices with both IoT and edge capabilities is not cost-effective. A better option is to deploy multiple cheaper IoT devices that generate data and connect all of them to a single edge server capable of processing data.

Stay a step ahead of competitors with pNAP's edge servers and ensure zero latency for IoT-driven systems regardless of where you set them up.

How Do IoT and Edge Computing Work Together?

Edge computing provides an IoT system with a local source of data processing, storage, and computing. The IoT device gathers data and sends it to the edge server. Meanwhile, the server analyzes data at the edge of the local network, enabling faster, more readily scalable data processing.

When compared to the usual design that involves sending data to a central server for analysis, an IoT edge computing system has:

Reduced latency of communication between the IoT device and the network.
Faster response times and increased operational efficiency.
The ability to continue operating even if the system loses connection with the cloud or the central server.
Smaller network bandwidth consumption as the system only streams data to the cloud for long-term storage or analysis.

Edge computing is an efficient, cost-effective way to use the Internet of Things at scale without risking network overloads. A business relying on IoT edge also lowers the impact of a potential data breach. If someone breaches an edge device, the intruder will only have access to local raw data (unlike what happens if someone hacks a central server).

The same "smaller blast radius" logic applies to accidental data leaks and similar threats to data integrity.

Additionally, edge computing offers a layer of redundancy for mission-critical IoT tasks. If a single local unit goes down, other edge servers and IoT devices can go on operating without issues. There are no single points of failure that can bring all operations down to a halt.

Deploying to the edge is on the rise across all verticals of enterprise IT. Learn what other strategies are currently making an impact in our article on cloud computing trends.

IoT Edge Computing Features

While each IoT edge computing system has unique traits, all deployments share several characteristics. Below is a list of 6 features you can find in all IoT edge computing use cases.

Consolidated Workloads

An older edge device typically runs proprietary apps on top of a proprietary RTOS (real-time operating system). A cutting-edge IoT edge system has a hypervisor that abstracts the OS and app layers from the underlying hardware.

Using a hypervisor enables a single edge computing device to run multiple OSes, which:

Paves the way for workload consolidation.
Reduces the physical footprint required at the edge.

As a result, the price of deploying to the edge is far lower than what you once had to pay to set up a top-tier edge computing system.

Pre-Processing and Data Filtering

Earlier edge systems typically worked by having the remote server request a value from the edge regardless of whether there were any recent changes. An IoT edge commuting system can pre-process data at the edge (usually via an edge agent) and only send the relevant info to the cloud. This approach:

Reduces the chance of data bottlenecks.
Lowers cloud storage and bandwidth costs.
Improves system response rates.

PhoenixNAP's Bare Metal Cloud enables you to reduce data transfer costs with our bandwidth packages. Monthly bandwidth reservations are a cost-optimal option for high-workload use cases, such as high-traffic websites, streaming services, or IoT edge devices.

Scalable Management

Older edge resources often used serial communication protocols that were difficult to update and manage at scale. A business can now connect IoT edge computing resources to local or wide area networks (LAN or WAN), enabling central management.

Edge management platforms are also increasing in popularity as providers look even further to streamline tasks associated with large-scale edge deployments.

Open Architecture

Proprietary protocols and closed architectures were common in edge environments for years. Unfortunately, these features often lead to high integration and switching costs due to vendor lock-ins, which is why modern edge computing relies on an open architecture with:

Standardized protocols (e.g., OPC UA, MQTT).
Semantic data structures (e.g., Sparkplug).

Open architecture reduces integration costs and increases vendor interoperability, two critical factors for the viability of IoT edge computing.

Want to learn more about system architectures? Check out our in-depth articles on cloud computing architecture, hybrid cloud architecture, and cloud-native architecture.

Edge Analytics

Earlier versions of edge devices had limited processing power and could typically perform a single task, such as ingesting data.

Nowadays, an IoT edge computing system has more powerful processing capabilities for analyzing data at the edge. This feature is vital to low-latency and high data throughput use cases traditional edge computing could not reliably handle.

Distributed Apps

Intelligent IoT edge computing resources de-couple apps from the underlying hardware. This feature enables a flexible architecture in which an app can move between compute resources both:

Vertically (e.g., from the edge resource to the cloud).
Horizontally (e.g., from one edge computing resource to another).

A business can deploy an edge app in three types of architectures:

100% edge. This architecture has all compute resources on-premise. This design is popular with organizations that do not wish to send data off-premises, typically due to security concerns. A business that is okay with heavy on-prem investments is also a typical adopter.
Thick edge + cloud architecture. This design includes an on-prem data center, a cloud deployment, and edge compute resources. A common choice for companies that already invested heavily in an on-prem data center but later decided to use the cloud to aggregate and analyze data (typically from multiple facilities).
Thin (or micro) edge + cloud architecture. This approach always includes cloud compute resources connected to one (or more) smaller edge compute resource. There are no on-prem data centers in this design.

Learn how to securely connect distributed apps and see how you can make the most out of this modern approach to IT.

IoT Edge Computing Use Cases

Edge computing can play a vital role in any IoT design that requires low latency or local data storage. Here are a few interesting use cases:

Industrial IoT (IIoT). IoT sensors can track the state of industrial machinery, identifying issues such as breakdowns or overuse. Meanwhile, an edge server can respond to problems before a potential disaster.
Self-driving cars. An autonomous vehicle driving down the road must be able to collect and process real-time data (traffic, pedestrians, street signs, stop lights, etc.). Automatic cars are a zero-latency use case, so using edge IoT is the only way to ensure that a self-driving car can stop or turn quickly enough to avoid an accident.
Visual inferencing. A high-resolution camera with an IoT edge computer can consume video streams and perform inferencing on the collected data. That equipment can detect people with high temperatures, intruders in restricted areas, safety violations, an anomaly on production lines, etc.
Automated truck convoys. IoT edge computing can also enable a business to create an automated truck convoy. A group of IoT-powered trucks can travel close behind one another in convoy, saving fuel costs and decreasing congestion. In that scenario, only the first truck would require a human driver.
Remote condition-based monitoring. In a scenario where a failure can be catastrophic (e.g., an oil or gas pipeline), using IoT edge to monitor the system is a no-brainer. An IoT sensor can track an asset's condition (e.g., temperature, pressure, stress, etc.), and the edge server can recognize and respond to a potential issue in milliseconds.

Edge computing is still a relatively novel concept, so it is natural that some companies are having a hard time deploying the technology. Our article on edge computing challenges explains the most common roadblocks and, more importantly, how to overcome them.

IoT Edge Computing: A Game-Changer for Enterprise IT

Today, the IoT sector operates across numerous scenarios without edge computing. However, as the number of connected devices grows and businesses explore new use cases, the ability to retrieve and process data faster will become a decisive factor. Expect IoT edge computing to play a major role in years to come as more and more companies start pursuing the benefits of zero-latency data processing.

Software Composition Analysis (SCA) Explained

Gone are the days of building software from scratch. Most development teams use open-source software (OSS) to speed up work and reduce time to market (TTM). Programmers use ready-made OSS components and write custom code to stitch everything together into a functioning piece of software.

While beneficial in terms of time and costs, using OSS as building blocks can introduce critical vulnerabilities or flaws to an application. This inherent risk is why software composition analysis is a must for any app that relies on open-source code.

This article is an intro to software composition analysis (SCA), a software engineering practice that enables teams to manage open-source components within apps. Read on to see how SCA helps identify, evaluate, and remove OSS-related risks.

Software composition analysis (SCA) explained

What Is Software Composition Analysis?

Software composition analysis is a security methodology that tracks and analyzes open-source packages within a codebase. SCA is vital when you consider the following figures:

More than 90% of modern apps use OSS packages.
An average app relies on 147 different open-source components.
An average organization is aware of less than 10% of its OSS use.

Software composition analysis has two primary goals:

Detect all open-source components within an app, including their supporting libraries and dependencies.
Check OSS components for missing patches, security flaws, and licensing issues.

SCA procedures rely on an "inventory, analyze, control" framework to give teams a full view of OSS usage.

Here's an overview of how this framework works:

Inventory. The first step is to inventory all OSS components and libraries within the software. Then, SCA generates a dependency tree that presents both direct dependencies (those explicitly included in the project) and transitive dependencies (those brought in indirectly through other dependencies).
Analyze. The second step is to check all OSS components for flaws or license-related issues. If there's a problem, the process generates contextual info (e.g., issue description, affected components, CVSS scores, relationship paths, etc.).
Control. Finally, SCA offers remediation guidance by providing information on mitigating the problem.

Reports generated from SCA go to the security personnel responsible for mitigating detected issues. In some CI/CD pipelines, an SCA-identified problem may block new commits to the codebase. Here's an overview of what issues SCA detects:

Known security vulnerabilities in third-party components.
License compliance issues.
Outdated or end-of-life OSS components.
Security misconfigurations.
Cryptographic vulnerabilities (e.g., weak algorithms, outdated cryptographic libraries, insufficient key lengths, etc.).
Components that may lead to the exposure of sensitive data (e.g., hardcoded credentials or configurations that inadvertently leak confidential data).

SCA is a standard part of DevSecOps, a development practice that integrates security into every phase of the software development lifecycle (SDLC). SCA enables OSS checks in the early stages of development before issues reach the build stage.

Why Is Software Composition Analysis Important?

Software composition analysis ensures the security, compliance, and reliability of open-source components within software. Here's an overview of all the benefits of adopting SCA:

Improved security. SCA helps identify and fix software vulnerabilities, reducing the risk of security breaches, data leaks, and non-compliance.
Dependency management. Modern software relies heavily on third-party libraries and open-source components. SCA helps manage dependencies by providing visibility into components and the way they interact with each other.
SBOMs. SCA generates a software bill of materials (SBOM) that gives complete visibility into all proprietary and open-source components. This info helps stakeholders understand security risks and licensing requirements.
License compliance. Open-source software comes with various licenses, each with its own terms, permissions, and restrictions. SCA ensures compliance with all OSS licenses and prevents costly legal issues.
Vulnerability detection. SCA identifies and alerts developers to flaws in OSS components. Teams then take proactive measures to update or replace vulnerable components.
Risk prioritization. SCA prioritizes vulnerabilities based on various factors (the flaw's severity, the importance of the component, the availability of updates, etc.). These assessments help the team focus efforts on addressing the most critical risks.
Enhanced team productivity. SCA automates a range of processes and frees up developers from manual tasks. Developers also get more freedom to innovate due to less worry about potential OSS incidents.
Cost savings. Detecting and addressing security vulnerabilities early in development is far more cost-effective than dealing with post-release security incidents.
Quicker, safer TTM. Companies that use SCA speed up time to market by enabling teams to use OS components to expedite work. The product reaches the market faster and with fewer delays. Additionally, the product becomes safer and has less risk for license non-compliance.

Concerned about high IT expenses? Check out our article on IT cost reduction to see 12 tried-and-tested strategies for lowering IT expenses.

Challenges of Software Composition Analysis

While software composition analysis is valuable when managing third-party software components, SCA has a few must-know drawbacks. Here are the usual challenges companies face once they adopt SCA:

Limited scope. SCA only identifies risks in open-source components, not in custom (original) code. Teams must pair SCA with tools for proprietary code (like SAST, DAST, IAST, or RASP) to get a complete suite of app security tests.
Incomplete component inventories. Dynamic development environments have frequent changes. Regular updates often lead to discrepancies in the component inventory. Keeping a comprehensive and up-to-date inventory of all components in a project is often challenging.
False positives and negatives. SCA tools occasionally generate false positives (incorrectly flagging a component as vulnerable) or false negatives (failing to detect an actual vulnerability). False positives lead to unnecessary work, while false negatives lead to unaddressed security risks.
Workflow integration. Integrating SCA seamlessly into existing development workflows is challenging. Most companies face a complex and labor-intensive deployment. Also, some teams resist using a tool that disrupts usual processes or introduces delays.
No auto-remediation. Most SCA tools only identify vulnerabilities and provide remediation guidance without offering auto-mitigation of issues.
Transitive (indirect) dependencies. Identifying and understanding the security implications of indirect dependencies is complex, and some SCA tools struggle to provide complete visibility into the entire dependency tree. The fact that 80% of vulnerabilities detected by SCA are in transitive dependencies further underlines this concern.
Database vulnerability lag. Many vulnerability databases and SCA tools rely on fail to add freshly discovered flaws in a timely fashion.
Resource constraints. Implementing and maintaining an SCA process requires dedicated resources for training, monitoring, and responding to security issues. Some organizations face resource constraints in terms of time, budget, or expertise.

Deploying SCA to a large codebase often reveals a sizable code debt. Learn more about this issue and the most effective ways to deal with it in our article on technical debt.

What Are Software Composition Analysis Tools?

Software composition analysis tools help organizations identify, analyze, and manage third-party components and open-source software used in their apps. These tools are crucial in addressing potential vulnerabilities, licensing issues, and other risks of using external code.

Here are a few of the most popular SCA tools currently on the market with an overview of their main selling points:

Snyk provides automated scans, real-time incident alerts, and a vast custom database of software vulnerabilities.
Qwiet AI uses AI to detect zero-day and pre-zero-day vulnerabilities in OSS code.
Spectral provides automated scanning, customizable policies, and advanced rule creation.
Mend is an enterprise-grade SCA platform that easily handles large numbers of apps and developers.

Here are the five different types of SCA tools based on their functionalities:

Package managers that help install and manage open-source packages, plus provide info on dependencies to avoid version conflicts.
Binary analysis tools that scan compiled code to identify open-source components and offer info on dependencies, security risks, and licensing issues.
Vulnerability scanners that scan open-source packages and libraries for weaknesses.
Continuous monitoring tools that track apps and alert developers to changes or updates to OSS components.
Composition analysis tools that provide a comprehensive view of OSS components, plus analyze dependencies, license issues, and overall software health.

Many software composition analysis tools provide features from two or more of these categories. Others are more specialized, so adopters often combine several tools to ensure well-rounded SCA capabilities.

How SCA Tools Work?

SCA tools integrate seamlessly into development workflows and CI/CD pipelines. Once integrated, the tool automatically scans the code for issues and provides real-time feedback.

Here's an overview of how SCA tools work:

Component identification. The first step is to scan the source code and the associated artifacts used to compile the app. The tool identifies all OSS components.
Dependency discovery. The SCA tool analyzes OSS components to identify all relevant dependencies (direct and transitive).
Vulnerability detection. The tool compares all identified components against a database of known vulnerabilities and flaws. This process often involves referencing the Common Vulnerabilities and Exposures (CVE) database, but many tools use additional proprietary resources.
Policy enforcement. Many SCA tools enable you to apply security and license policies at scale. If an admin defines policies related to OSS security (e.g., rules for acceptable risk levels or license types), the SCA tool flags all components that do not meet the set criteria.
License analysis. The tool analyzes the licensing info associated with each OSS element. The SCA platform also checks whether licenses comply with the organization's policies and relevant legal requirements.
Risk scoring. The tool assigns a risk score to each detected vulnerability. The usual scoring factors are severity, exploitability, and the number of dependencies.
Reporting. SCA tools generate reports that offer insights into software composition, vulnerabilities, and the overall OSS security posture.
OSS monitoring. SCA is not a one-time process. Tools continually monitor for updates to vulnerability databases and changes in the status of third-party components. The tool alerts admins of any newly detected flaws or license conflicts.
Remediation guidance. The SCA tool offers guidance on how to remediate detected issues. Most tools provide info on available updates, patches, or alternative components that address current security or licensing concerns.

Learn about vulnerability assessments and see why a proactive search for flaws should be at the top of any security team's to-do list.

Software composition analysis best practices

What to Consider When Choosing a Software Composition Analysis Tool?

Here are a few considerations to keep in mind when choosing an SCA tool:

Ease of use. Your SCA tool must be as simple to use and unintrusive to workflows as possible. Look for intuitive interfaces, clear documentation, and features that simplify the configuration and management of the tool.
Tech compatibility. Ensure the SCA tool supports your software development stack's programming languages and technologies.
Team fit. An SCA tool must not require developers to learn new software from scratch. Find a tool that fits your team's current skill set and preference.
Vulnerability databases. Check the accuracy and comprehensiveness of the tool's vulnerability database(s). A reliable SCA tool relies on up-to-date and widely recognized databases.
Community support. Consider the community around the SCA tool and check for user feedback and reviews. A vibrant community and positive feedback indicate a tool's effectiveness and reliability.
License analysis. Assess the accuracy and thoroughness of the license analysis provided by the SCA tool. Ensure that the tool effectively identifies and manages OSS licenses.
Scalability. Evaluate whether the SCA tool is scalable enough to meet the demands of your project (both currently and in the future).
Integration. A SCA tool must seamlessly integrate with existing development workflows, including CI/CD pipelines, issue tracking systems, and version control systems. The tool must work within existing workflows so developers don't have to exit development environments to implement security functions.
Reporting and analytics. Clear, correct, informative, and actionable reports are essential for effective SCA. Consider the reporting and analytics capabilities of every potential SCA tool.
Policy enforcement. Not all SCA tools allow you to define and enforce policies. Check whether the tool has policy enforcement capabilities if you plan to set predefined guidelines and standards.

Remember that SCA must not interrupt the development process or force teams into steep learning curves. Seamless integration with both systems and personnel is vital for maximizing the benefits of an SCA tool.

An Absolute Must for Any OSS-Based App

The growing adoption of OSS has made SCA vital to application security. Software composition analysis enables teams to use open-source packages without exposing companies to unnecessary vulnerabilities or legal issues. Use SCA to pre-empt risks and grant your developers the freedom to safely use OSS as building blocks when creating software.

TDD vs. BDD: Differences and Use Cases Explained

Test-Driven Development (TDD) and Behavior-Driven Development (BDD) are development methodologies that enhance software reliability and quality. Both approaches guide developers through the process of writing and testing code, but they focus on slightly different aspects of software development. Understanding the nuances will help you choose the right approach for your projects.

This article compares TDD and BDD, explaining their core principles, advantages, disadvantages, and use cases.

What Is Test-driven Development (TDD)?

Test-driven development emphasizes the creation and execution of automated tests before the development of the actual software code. This methodology revolves around a short, repeatable development cycle to ensure the codebase remains error-free and robust.

TDD operates under a simple but strict mantra: "Red, Green, Refactor." This cycle involves the following steps:

Red: Write a Failing Test. The first step in TDD is to write a test that defines a desired improvement or new function. This test should initially fail because the feature it is testing does not yet exist.
Green: Make the Test Pass. Once the failing test is in place, the next step is to write the minimal code necessary to pass the test. This step often requires just enough implementation to fulfill the test's requirements without concern about perfect design or optimization.
Refactor: Improve the Code. With the test now passing, the code is then refactored. This step may involve reorganizing the code, removing duplication, and making other improvements to ensure the code is clean, efficient, and well-structured. Notably, the functionality, as defined by the tests, should not change during refactoring.

Example of the refactor cycle with red and green.

TDD Features

Test-driven development (TDD) is structured around several fundamental principles that guide the coding and testing process:

Test first approach. This principle requires that before developers write any functional code, they must first write an automated test for the new feature or functionality they intend to implement. This test initially fails (the "Red" phase in TDD), which is expected since the corresponding functionality does not yet exist. Writing tests first ensures that development efforts are focused solely on passing the tests with the minimum code necessary, avoiding over-engineering, and keeping the codebase simple and clean.
Short development cycles. TDD emphasizes short, manageable cycles of development, often referred to as iterations. Each cycle involves writing a test, passing it, and refactoring, allowing developers to focus on small, incremental changes. The benefit is twofold: it minimizes the risk associated with changes by isolating them into small, controllable portions and allows for continuous improvement in code functionality and structure. Short cycles also enable developers to adapt to changes in requirements or design decisions, as the impact of each change is confined to a small project segment.
Refactoring. Refactoring involves improving the internal structure of the code without changing its external behavior. This process simplifies algorithms, removes duplicate code, and renames variables for clarity. Regular refactoring helps avoid technical debt and ensures that the code remains easy to understand, modify, and extend. Importantly, because tests cover functionality, developers can refactor with confidence, knowing that they'll be alerted if a change inadvertently alters the correct behavior of the code.
Immediate feedback. The principle of immediate feedback allows TDD to create high-quality software. As developers write tests and the corresponding code, they run them frequently—often several times an hour. This process provides them with continuous, immediate feedback on how their changes affect the existing codebase. Immediate feedback is critical for catching issues early in development, significantly reducing the debugging and troubleshooting time typically required later in the project lifecycle.

TDD Advantages

The goal of TDD is to write cleaner, bug-free code from the outset. This leads to several key outcomes:

Robustness. Developers ensure the codebase's robustness by writing tests covering each function before implementation. Each new feature starts by meeting the documented expectations, which guards against future changes that could break existing functionality.
Fewer bugs. Since TDD requires tests for every new feature before it is implemented, it greatly reduces the likelihood of bugs in the code as tests catch regressions and errors early in the development cycle.
Better design decisions. TDD encourages developers to consider structuring their code to make it testable, often leading to better overall design. By focusing on the requirements one at a time, developers are also encouraged to build modular, decoupled systems.
Documentation. Automated tests serve as live documentation for the code. They explain what the code does more clearly and concisely than traditional documentation, as they must be updated alongside the code to remain passing.

TDD Disadvantages

Here are the challenges of test-driven development:

Upfront design requirement. TDD requires developers to consider their application's design at the start, promoting a well-thought-out architecture but demanding more initial planning and time.
Learning curve and development pace. The TDD cycle can initially slow development as developers adapt to writing tests before the code.
Potential for overengineering. Balancing the depth and breadth of testing without overcomplicating the test suite is a key challenge in TDD. Developers might spend excessive time designing tests for unlikely scenarios, diverting resources away from more essential development tasks.
Discipline in test writing. Successful TDD demands a high level of discipline and a deep understanding of how to accurately write meaningful tests that reflect user needs and system requirements.
Integration challenges. Integrating TDD into a software development life cycle can be difficult, especially in large organizations with established workflows. The shift to TDD may require significant changes in team dynamics, project management, and the overall culture.

TDD Use Cases

Here are the cases where the use of TDD is particularly compelling:

New projects. Starting with TDD can be helpful for a new software development project. TDD establishes a solid foundation by ensuring every feature added to the project has a corresponding test. This practice helps maintain high code quality, and as the project grows, the comprehensive suite of tests developed from the start serves as a safety net, allowing new features to be added and existing ones to be modified or extended.
Projects with precise requirements. TDD excels in environments where project specifications are well-defined and stable. In these cases, developers can write tests that directly reflect these requirements. The direct alignment between tests and specifications ensures that all functionality meets the predetermined criteria. Writing tests first also helps identify any potential misunderstandings or ambiguities in the requirements early in the development process, allowing for clarification before significant resources are invested. Furthermore, the iterative nature of TDD means that each requirement is systematically addressed, tested, and integrated into the larger project, ensuring comprehensive coverage and implementation.
Safety-critical systems. TDD is particularly suited for environments where software failures lead to significant harm or cost, such as in medical devices, aerospace, automotive safety systems, and financial systems. The safety and security provided by having a thorough suite of tests means that the software is verified at every stage of its development, significantly reducing the risk of critical bugs or vulnerabilities going undetected. Additionally, in regulated industries, the extensive documentation the tests provide is vital to complying with industry standards and audits.

Unsure which software testing methodologies are right for your project? Our article explores different approaches to ensure high-quality software that meets user needs.

What Is Behavior-Driven Development (BDD)?

Behavior-driven development is a development practice that extends the principles of TDD by focusing more explicitly on software behavioral specification. This approach is fundamentally user-centric, emphasizing the creation of software that meets the business and user requirements by encouraging collaboration and understanding among all stakeholders involved in a project.

BDD aims to shift the focus of software development from merely writing functional code to ensuring that the software behaves exactly as the stakeholders expect. To achieve this, BDD uses simple, domain-specific language to describe the outcomes and behaviors of the system. Stakeholders create these descriptions collaboratively and express them clearly to technical and non-technical participants.

Here is how BDD works in practice:

Defining behavior. BDD starts with stakeholders, including developers, testers, business analysts, and product owners, collaboratively defining behaviors using a common language. This language simplifies complex software concepts into terms easily understood by everyone involved.
Writing scenarios. These behaviors are translated into specific, real-world scenarios that describe how the software should behave in various situations. These scenarios are written in a "Given-When-Then" format, which outlines the conditions, the action taken, and the expected outcome. This method, known as “Specification by Example,” helps ensure clarity and completeness in the behavior specification.
Implementing code. Developers then write code to pass these tests, ensuring the software behaves as agreed upon. This process is often iterative, where failing tests guide the development until all tests pass, confirming that the software meets the defined behaviors.
Automating tests. Once scenarios are defined, they are converted into automated tests before any functional code is written. These tests serve as living documentation and a checklist that the software must pass.
Refining and iterating. BDD facilitates continuous feedback and iterations as development progresses, allowing behaviors to be refined and extended based on ongoing discussions among stakeholders. This iterative cycle helps catch misunderstandings and misalignments early, significantly reducing the cost and effort of changes.

BDD Features

Behavior-driven development (BDD) incorporates several distinctive features that streamline communication among project stakeholders:

Ubiquitous language. BDD introduces a shared, domain-specific language that unifies communication across different teams, including developers, QA engineers, and non-technical stakeholders. This standardized language reduces miscommunication and ensures everyone understands project details and requirements, enhancing overall project coherence.
Specification by example. BDD defines software behavior using concrete, real-world scenarios. These examples act as precise, actionable specifications for development, allowing teams to visualize how software features should interact with users. This practice aids in developing intuitive and effective software and serves as living documentation.
Collaboration. BDD fosters extensive collaboration among all stakeholders, from the initial planning to ongoing development. This inclusive approach ensures that development remains aligned with business objectives and user needs, facilitating a more targeted and effective product development cycle.
Outside-in approach. This method prioritizes user needs and system behaviors over technical specifications. By focusing on user experiences and business goals from the outset, BDD ensures that development efforts are directly aligned with end-user expectations and company objectives, which leads to more relevant and successful software solutions.

BDD Advantages

BDD offers a range of advantages that contribute to a more efficient and effective development process:

Enhanced communication. Using a common language and the active involvement of stakeholders throughout the development process fosters better understanding and clearer communication across all team members.
Focus on user experience. BDD ensures software development meets user expectations and needs, resulting in valuable, user-friendly products.
Continuous feedback. Regular feedback loops with stakeholders throughout the development process enable more accurate and adaptive development, helping teams quickly resolve issues or adjust to changing needs.
Fewer misunderstood requirements. Stakeholders' early and continuous involvement helps clarify and solidify requirements, reducing the risk of misunderstandings and ensuring that the final product faithfully meets the intended specifications.

BDD Disadvantages

Despite its benefits, BDD also presents several challenges:

Requires involvement from various teams. Effective BDD implementation demands broad and continuous collaboration, which can be challenging to coordinate and manage, especially in larger or dispersed teams.
Time-consuming. Writing and maintaining detailed behavior specifications is time-consuming and may slow development.
Possible overhead. The additional layer of behavior specification introduces complexity and overhead. If not properly managed, this can increase project costs and extend timelines, impacting overall project efficiency.
Tool dependent. BDD relies heavily on tools for managing and executing behavior specifications, introducing potential dependencies and complexities that need careful management to avoid disrupting the development process. These tools, such as Cucumber, SpecFlow, and Behat, help translate plain language descriptions into executable tests that validate the software's behavior against the specified requirements.

BDD Use Cases

BDD is particularly effective in scenarios where clear understanding and communication are crucial:

Complex systems involving multiple stakeholders. BDD is ideal for complex projects requiring the integration of multiple perspectives. It ensures that all stakeholders' needs are considered and addressed in the final product.
Projects with evolving requirements. BDD is well-suited to environments where requirements may change over time, providing the flexibility to adapt effectively to new information and user feedback.
User-centric applications. For applications where user satisfaction is critical to business success, BDD helps align the product with user expectations and preferences, enhancing user satisfaction and product viability.
Integration with agile processes. BDD complements agile methodologies, enhancing adaptability and team synergy. It is an excellent choice for teams that use agile frameworks to manage and execute their development processes.

Our article on DevOps vs. Agile unpacks the strengths of each approach to help you choose the right path for your software development.

TDD vs. BDD: Comparison

Here is a detailed comparison table between TDD and BDD, highlighting their distinct aspects.

	Test-Driven Development (TDD)	Behavior-Driven Development (BDD)
Primary focus	Focuses on the implementation of functionality through a test-first approach.	Focuses on collaboration and understanding the behavior of the system from the user's perspective.
Language and readability	Utilizes code-specific terminology in test cases.	Employs natural language (often Gherkin) in scenarios, making it accessible to both technical and non-technical team members.
Collaboration and communication	Primarily involves developers and testers.	Encourages collaboration among developers, testers, and business stakeholders to define and validate behaviors.
Level of abstraction	Concentrates on low-level unit tests to verify individual code units.	Emphasizes higher-level tests simulating user interactions or end-to-end scenarios.
Test organization	Organized according to code structure, often following a hierarchical or modular approach.	Organized around user-desired behaviors, generally grouped by specific features or functionalities.
Purpose	Aims to ensure code correctness through automated tests.	Promotes a shared understanding and validation of system behavior, improving communication.
Development workflow	Involves writing tests before coding the corresponding functionality.	Begins with collaborative scenario definitions, followed by coding. Often incorporates TDD principles within BDD.
Test scope	Narrow, focusing on individual code units.	Broad, encompassing multiple units to verify complete system operations.
Test case style	Technical and focused on code implementation details.	User-focused, emphasizing behavior and outcomes.
Test granularity	Fine-grained, with tests targeting specific code units in isolation.	Coarser-grained, targeting overall system behavior and integration.
Iterative refinement and feedback	Iterative code refinement driven by test results and modifications.	Iterative development shaped by ongoing collaboration and feedback on behavior specifications.

Choosing the Right Development Methodology for Your Project

TDD and BDD are robust frameworks for software development, each with unique benefits and use cases.

TDD is particularly effective in projects where code correctness and reliability are vital. Its test-first approach ensures that every piece of code is backed by tests, leading to fewer bugs and a more maintainable codebase.

On the other hand, BDD extends the principles of TDD by focusing on the system's overall behavior and involving non-technical stakeholders in the development process. This collaborative approach emphasizes the importance of every team member's role, enhancing communication between developers, testers, and business stakeholders. BDD is particularly beneficial for complex projects where understanding user interactions and achieving a common understanding across a diverse team are critical.

The choice between TDD and BDD should be guided by the project's specific needs, the team's workflow, and the desired level of stakeholder involvement. While TDD is centered around technical correctness, BDD aims at broader collaboration and validating user-focused outcomes.

Ultimately, both TDD and BDD aim to enhance development efficiency, reduce misunderstandings, and deliver high-quality software that meets user expectations and business goals. Development teams may find it beneficial to combine elements of both methodologies to leverage their advantages.

What Is DevSecOps? Benefits, Workflow, Best Practices

Companies wishing to deliver secure software to their users can no longer afford to treat security as an afterthought. In today’s digital landscape, security must be an inherent feature of every software solution.

Enter DevSecOps, an approach that integrates security measures and practices at every step of the software development lifecycle (SDLC), from planning and coding to testing, deployment, and monitoring.

In this article, we will examine the principles, practices, and benefits of DevSecOps, demonstrating how this approach helps businesses deliver robust and secure software solutions efficiently and quickly.

What Is DevSecOps?

DevSecOps (short for Development, Security, and Operations) is a software development practice that integrates security into every phase of the software development lifecycle. With DevSecOps, security is embedded into the software as it is developed rather than added later.

In the past, security practices and features were only considered at later stages in the software development lifecycle and were typically dealt with by a separate security team. However, rapidly evolving cybersecurity threats have necessitated the practice of integrating security from the very start and maintaining it throughout the CI/CD pipeline.

DevSecOps vs. DevOps

The DevSecOps and DevOps approach both aim to streamline and accelerate software development and delivery by enhancing collaboration between development and operations teams and automating repetitive tasks. The difference between the two is that DevSecOps places security concerns at the forefront during all phases of the software development lifecycle.

While DevOps remains fast and efficient, DevSecOps is more likely to identify and mitigate potential security vulnerabilities, thereby reducing the risk of data breaches and leaks.

DevSecOps Components

To achieve its security goals, DevSecOps involves various components and practices:

Collaboration and communication. DevSecOps fosters collaboration between the development, security, and operations teams. To achieve this aim, the teams must ensure there are no communication gaps between them and that all participants understand that they are working towards a common goal.
Security awareness and ownership. As an extension of the principle of collaboration, everyone involved in the SDLC must be aware of the security imperative and have a sense of ownership over the results. DevSecOps is founded on the principle that “security is everyone’s responsibility”.
Security of tools and architecture. The creation of secure applications requires a secure environment. Before any system is used, the security team must test the configurations to ensure they can resist malicious activity. This principle also incorporates the implementation of stringent security measures during the entire SDLC, including multi-factor authentication, least-privileged access, and the segregation of CI/CD pipelines to prevent lateral movement.
Automation. Central to the DevSecOps approach is the automation of security testing and analysis throughout the CI/CD pipeline. This allows for the rapid delivery of new software that has passed vulnerability scanning, code analysis, and compliance checks. With integrated security controls, whenever the risk level rises over a certain level, the building process stops until the security team resolves the issue.
Security testing. With DevSecOps, security testing is conducted on an ongoing basis, throughout the development process. The range of tests that ensure the delivery of secure software includes static application security testing (SAST), dynamic security testing (DAST), penetration testing, compliance checks, and others.

What Does a DevSecOps Workflow Look Like?

A DevSecOps workflow emphasizes collaboration, automation, and the proactive implementation of security measures at every stage of the SDLC. While specific workflows vary depending on the organization’s tools and needs, a general DevSecOps workflow might include the following:

Planning. Involves defining the security requirements and objectives, identifying potential threats and vulnerabilities, and conducting threat modeling. From these activities, developers and architects can design security controls directly into the application’s architecture.
Coding and development. Developers write code following secure coding practices, the code is peer reviewed, and automated static application security tools (SAST) analyze the source code for vulnerabilities.
Continuous integration (CI). Changes introduced to the code trigger automated building and testing and developers receive immediate feedback if security issues are detected.
Continuous deployment (CD). Container images are scanned for vulnerabilities in the staging environment before deployment to container orchestration platforms via automated dynamic application security testing (DAST) tools.
Operations and monitoring. Applications and infrastructure are monitored in production for security anomalies and threats; automated cyber incident detection and response are implemented to mitigate potential security issues and breaches.
Remediation and feedback. Vulnerabilities and weaknesses are remediated according to priority, and their root causes are analyzed and addressed. All parties involved receive feedback that helps them to improve security practices.
Education and sharing knowledge. Security awareness programs and training are ongoing practices, and teams are encouraged to share knowledge and collaborate around a common goal.
Compliance. Automated compliance checks ensure that all processes adhere to security standards as well as industry regulations (PCI DSS, GDPR, HIPAA, etc.).
Reporting. Compliance checks, alerts, and test results are collated into reports that are shared with stakeholders and used for auditing purposes.

DevSecOps Benefits

By using the DevSecOps approach and integrating security practices into their software development and delivery process, organizations reap numerous benefits, including:

Enhanced security. Proactive and constant monitoring for security vulnerabilities reduces the risk of security breaches and data leaks.
Faster and more reliable software delivery. The automation of CI/CD pipelines enables the speedier delivery of consistently secure software that has been scanned and tested.
Better collaboration. DevSecOps rests on the collaboration between developers, security experts, and operations teams, who all share the responsibility for delivering a safe product.
Cost reduction. Early detection of security issues is more cost-effective than having to fix them in production.
Full compliance. The automation of compliance checks at every step ensures that software development adheres to regulation and security standards.
Risk management. DevSecOps incorporates threat modeling and regular vulnerability scanning of code, dependencies, and infrastructure to minimize the likelihood of weaknesses being exploited.
Enhanced incident response. The automation of incident detection and response mechanisms helps organizations to quickly respond to security incidents.
Flexibility and scalability. DevSecOps practices integrated well with cloud-native architectures, enabling organizations to easily scale their operations and adapt to changing business needs.
Higher quality software. When security is embedded into the software development process, technical debt is reduced and the result are applications that are of greater quality and require less maintenance.
Competitive advantage and trust. Organizations committed to security build trust with their customers and stakeholders, potentially gaining a competitive advantage in the market.

DevSecOps Challenges

While it offers numerous benefits, DevSecOps also comes with a set of challenges:

Cultural shift. A DevSecOps approach requires that teams embrace collaboration and share responsibility for security. This may create resistance, especially in organizations with long-standing silos.
Security tool integration. Choosing the right security tools and integrating them into the CI/CD pipeline to ensure they are compatible with development and deployment tools can be time- and resource-consuming.
Skills and training. Not all development and operations teams have sufficient security knowledge and may require additional training. Organizations need to make an effort to enhance security awareness among all team members.
Automation challenges. Implementing automated security scanning tools is a complex task. Furthermore, automated scanning can create false positives that must be addressed manually.
Developer overload. Repeated security alerts can lead to alert fatigue among developers, potentially resulting in important issues being overlooked.
Speed vs. security. Finding the right balance between development speed and robust security testing can be difficult, as too many security checks slow down the development process.
Resource limitations. Organizations with budget and staff constraints may find it difficult to adequately implement DevSecOps practices.
Legacy systems. DevSecOps practices can be difficult to integrate with legacy systems and monolithic applications.
Complex environments. It can be difficult to manage security across multi-cloud and hybrid environments using diverse technologies.

DevSecOps Best Practices

Organizations wishing to effectively implement DevSecOps throughout the SDLC should follow these best practices:

Shift left. This means that security practices are implemented earlier in the development process, right from the design stage.
Adopt automation. The entire CI/CD pipeline should be automated to enable the rapid and consistent delivery of software, with a security check at every stage.
Implement security as code. Security controls should be embedded into the infrastructure code.
Maintain secure coding standards. Ensure that developers follow coding best practices and guidelines so that applications are secure from the start.
Foster a culture of collaboration. The development, security and operations teams should be aware of their shared responsibility and be encouraged to collaborate and share skills and knowledge.
Test early and regularly. Scan your code, dependencies, and infrastructure early in the development process and continue to conduct tests regularly.
Create feedback loops. Establish feedback loops so that teams can learn from security incidents and improve security practices.
Enforce access controls. Access controls such as least-privilege principles and role-based access controls should be implemented throughout the development process to protect sensitive data and systems.
Prioritize risk management. DevSecOps teams that make risk management their priority ensure that security controls are implemented, and risks reduced.
Manage third-party risks. Assess the risks associated with third-party software and regularly perform code dependency checks.
Maintain scalability. Ensure your DevSecOps practices can scale to accommodate evolving environments and meet your organization’s growing needs.

20 DevSecOps Tools to Consider

The following list contains some of the more widely recognized and comprehensive DevSecOps tools:

Checkmarx. An AI-powered software security solution that identifies and fixes vulnerabilities in source code.
Fortify. Offers static, dynamic, and interactive testing tools that find security issues in source code.
Veracode. A cloud-based application security platform that integrates security analysis into the CI/CD pipeline.
Snyk. A tool that finds and fixes vulnerabilities in code, dependencies, containers, and IaC.
OWASP ZAP. An open-source tool for finding vulnerabilities in web applications.
Burp Suite. A suite of security tools used for penetration testing of web applications.
WhiteHat Sentinel. Offers a scalable application security tool that integrates across the whole SDLC pipeline.
Nexus Lifecycle. A security tool that scans and checks for vulnerabilities in open-source libraries and products.
Codacy. An automated tool that reviews code via static code analysis.
SonarQube. An open-source platform offering plugins that identify code bugs during development.
Aqua Security. A security platform for cloud-native apps with full CI/CD integration.
Spectral. A security solution that scans for exposed secrets and security misconfigurations during build.
Clair. A container vulnerability scanner that analyzes container images and reports on known vulnerabilities.
ThreatModeler. A security tool that allows developers to create threat models for their applications, helping to identify weaknesses and vulnerabilities.
Anchore Engine. An open-source tool that analyzes container images and reports on CVE.
Cloud Foundry. An open-source, multi-cloud PaaS solution that protects containerized environments and cloud-native applications, ensuring security and compliance with regulations.
Palo Alto Networks Cortex XSOAR. A security orchestration automation and response (SOAR) platform that helps organizations investigate and respond to threats quickly.
OpenSCAP: A security content automation protocol (SCAP) framework that checks for compliance violations and security vulnerabilities.
DoControl. A tool that manages access privileges and data exposure by automating data access controls.
TheHive: An open-source incident response and case management platform with vulnerability tracking and management features.

DevSecOps: Where Innovation Meets Security

DevSecOps represents a cultural shift in the world of software development. This holistic approach gives security paramount importance while fostering collaboration and communication, whereby development, security, and operations teams work together to achieve common security goals.

By incorporating security practices at every stage of the SDLC, organizations proactively identify and remedy vulnerabilities in their applications, reducing the risk of breaches while delivering innovative software faster and more reliably. DevSecOps is the natural response to a continually evolving digital landscape, where safety and efficiency must go hand in hand.

Penetration Testing: Types, Tools, and Best Practices

Identifying and eliminating vulnerabilities in systems and applications is a priority in cybersecurity. Organizations use various techniques to uncover software flaws, but penetration testing is the most realistic and comprehensive method for analyzing security weaknesses. By simulating real-world cyber attacks, penetration testing assesses the effectiveness of security measures and exposes vulnerabilities that might otherwise remain undetected. Understanding how penetration testing works and how organizations leverage these tests to prevent costly and damaging breaches is essential for strengthening cybersecurity defenses.

This article provides an in-depth introduction to penetration testing, its methodologies, stages, tools, and its vital role in protecting digital assets.

What Is Penetration Testing?

Penetration testing is a systematic attempt to evaluate the security of an IT infrastructure by safely exploiting vulnerabilities. These vulnerabilities may exist in operating systems, services, applications, improper configurations, or risky end-user behavior. The primary goal of penetration testing is to identify security weaknesses before malicious actors can exploit them, thereby preventing data breaches, financial losses, and damage to an organization's reputation.

Penetration testers, often called ethical hackers, use the same tools, techniques, and processes as attackers to find and demonstrate the business impacts of weaknesses in a system. By simulating real-world attacks, penetration testing provides a realistic assessment of an organization's security posture, enabling stakeholders to prioritize remediation efforts effectively.

Types of Penetration Testing

Penetration testing is classified into various types based on the scope, objectives, and the amount of information shared with the testers.

Black Box Penetration Testing

In black box penetration testing, the tester has no prior knowledge of the target system's internal workings. The tester approaches the assessment as an uninformed cybercriminal would, relying solely on publicly available information and the tester's skills to identify vulnerabilities.

This method provides a realistic simulation of external threats but requires more time due to the extensive reconnaissance involved.

White Box Penetration Testing

White box penetration testing provides the tester with comprehensive information about the target system, including network diagrams, source code, credentials, and architecture details.

This approach allows for an in-depth examination of the security posture, focusing on internal vulnerabilities such as insecure coding practices and system misconfigurations. White box testing is efficient and thorough but may not accurately represent an external attacker's perspective.

Read our in-depth comparison of white and black box testing, the two most common setups for a penetration test.

Grey Box Penetration Testing

Grey box penetration testing is a hybrid approach where the tester has partial knowledge of the target system. This knowledge could include limited access credentials or partial architectural information. Grey box testing aims to simulate an attack by an insider or a hacker who has gained unauthorized access to an organization's network.

This method balances the depth of white box testing with the realism of black box testing.

External Penetration Testing

External penetration testing evaluates the security of an organization's external-facing assets, such as web applications, websites, email servers, and network infrastructure accessible from the Internet. The objective is to identify vulnerabilities external attackers could exploit to gain unauthorized access to internal systems and data.

Internal Penetration Testing

Internal penetration testing simulates an attack from within the organization's network. This type of testing assesses the potential impact of an insider threat, such as a disgruntled employee or an attacker who has breached the external defenses and gained access to internal systems. Internal testing evaluates the effectiveness of internal security controls and policies.

Targeted Testing

Targeted testing involves collaboration between the organization's IT team and penetration testers. Both parties know about the testing activities, allowing for real-time feedback and adjustments. This approach is often used to test specific aspects of the security infrastructure or to train the internal security team.

Blind Testing

In blind testing, the penetration tester is provided with limited information, typically only the organization's name. The security team is aware that a test is occurring but does not have details about the tester's methodologies. Blind testing evaluates the organization's security monitoring and incident response capabilities.

Double-Blind Testing

Double-blind testing extends the blind testing approach by keeping both the penetration tester and the security team unaware of specific details. Only a few individuals within the organization are aware of the test. This method provides the most realistic simulation of a real-world attack, assessing both detection and response mechanisms without prior knowledge.

Penetration Testing Methodologies

Organizations typically rely on one of the five main standardized penetration testing methods:

OWASP (Open Web Application Security Project)

The OWASP Testing Guide is a widely recognized framework focusing on web application security. It outlines techniques for identifying and mitigating common vulnerabilities such as:

SQL injections. Exploiting input fields to execute unauthorized SQL commands.
Cross-site scripting (XSS). Injecting malicious scripts into web pages viewed by users.
Insecure direct object references (IDOR). Accessing unauthorized resources by manipulating parameters.
Security misconfigurations. Identifying weaknesses due to improper configurations.

OWASP provides tools like OWASP ZAP and resources such as the OWASP Top Ten to assist in improving software security through a community-driven approach.

OSSTMM (Open-Source Security Testing Methodology Manual)

OSSTMM offers a scientific and methodological approach to security testing, emphasizing quantitative metrics to measure operational security. It provides guidelines for testing various domains, including information systems, telecommunications, physical security, and social engineering.

Key features of OSSTMM include:

Quantitative metrics. Objective measurements for trust and risk assessment, aiming for repeatable and verifiable results.
Rules of engagement. Clear guidelines defining the scope and limitations of testing activities.
Comprehensive coverage. Addressing multiple security channels to provide a holistic assessment.

ISSAF (Information System Security Assessment Framework)

ISSAF is a broad framework for assessing information system security. It is beneficial for complex environments that require meticulous documentation. The framework covers both technical aspects (e.g., network security, application security) and non-technical aspects (e.g., policies, procedures, and organizational culture), ensuring a holistic evaluation of the security posture.

ISSAF also encourages using a variety of tools and methods to ensure thoroughness, including both automated and manual testing techniques.

PTES (Penetration Testing Execution Standard)

PTES defines a standard for penetration testing by outlining seven main sections to promote consistency and comprehensiveness:

Pre-engagement interactions. Establishing scope, goals, and legal considerations.
Intelligence gathering. Collecting information about the target organization.
Threat modeling. Identifying potential threats and vulnerabilities.
Vulnerability analysis. Scanning and analyzing systems for weaknesses.
Exploitation. Attempting to exploit identified vulnerabilities.
Post-exploitation. Assessing the impact and extent of access gained.
Reporting. Documenting findings and providing remediation recommendations.

NIST (National Institute of Standards and Technology)

NIST provides cybersecurity guidelines and best practices through its Special Publications. In particular, NIST SP 800-115 offers a technical guide to information security testing and assessment, covering comprehensive procedures for planning, execution, analysis, and reporting.

This framework is popular within high-danger industries like banking, communications, and energy. Compliance with NIST standards is often required for U.S. federal agencies, organizations handling government data, and American businesses in general.

Each standardized methodology offers a unique approach to penetration testing:
• OWASP focuses on web application security with techniques for common vulnerabilities.
• OSSTMM emphasizes a quantitative, scientific approach across multiple security domains for repeatable results.
• ISSAF provides detailed processes suitable for complex environments needing meticulous documentation.
• PTES ensures consistency by outlining all phases of the penetration testing process.
• NIST offers guidelines aligned with regulatory compliance, especially for government-related organizations.

What Are the Stages of Penetration Testing?

Penetration testing follows a structured process consisting of the following stages:

Planning and Reconnaissance

The planning and reconnaissance stage involves the initial preparation and information gathering required to conduct a successful penetration test. This phase sets the foundation by establishing clear objectives and collecting essential data about the target systems.

Defining Scope and Objectives

In this phase, the penetration tester collaborates with the organization to define the scope, objectives, and rules of engagement for the test. Clear communication ensures that both parties understand what will be tested and the desired outcomes. Key activities include:

Establishing goals. Identifying what the organization aims to achieve, such as uncovering vulnerabilities, testing incident response capabilities, or meeting compliance requirements.
Setting scope. Determining the specific systems, networks, applications, and assets to be tested, ensuring all critical areas are included.
Agreeing on rules. Outlining the testing boundaries, permissible techniques, and legal considerations to ensure that all activities are authorized and conducted safely.

Information Gathering

After defining the scope and objectives, the tester begins collecting as much information as possible about the target environment. This information is crucial for identifying potential attack vectors and planning effective testing strategies. Activities include:

Open-source intelligence (OSINT). Gathering data from publicly available sources such as websites, social media profiles, domain registrations, and public records to understand the organization's external footprint.
Network scanning. Using tools like Nmap to identify live hosts, open ports, and running services, which helps map the network and identify potential entry points.
Footprinting. Mapping the organization's network architecture, including domain names, IP address ranges, and infrastructure details, to gain a comprehensive view of the target environment.

Scanning and Enumeration

The scanning and enumeration stage involves a deeper analysis of the target systems to identify vulnerabilities and gather detailed information that could be exploited. Scanning and enumeration involve these activities:

Vulnerability scanning. In this phase, testers use automated tools to detect known vulnerabilities in operating systems, applications, and network devices. This process identifies weaknesses such as missing patches, outdated software, or exploitable misconfigurations.
Banner grabbing. By collecting information from service banners, testers determine software versions and other details that may reveal known vulnerabilities. Banner grabbing helps build an inventory of systems and applications to focus on during exploitation.
Service enumeration. Testers identify services running on open ports and analyze their configurations. Understanding which services are active and how they are set up allows testers to pinpoint potential vulnerabilities associated with specific applications or protocols.

Understanding the difference between vulnerability scanning and penetration testing will enable you to create a comprehensive security testing strategy.

Exploitation

The exploitation phase involves actively attempting to breach the security of the target systems by exploiting identified vulnerabilities. This stage demonstrates the potential impact of a successful attack. During the exploitation phase, testers perform the following actions:

Attack execution. Testers use exploit tools and scripts to bypass security controls and gain unauthorized access to systems, applications, or data.
Privilege escalation. After initial access, testers attempt to increase their level of access to obtain higher privileges, allowing them to control more critical parts of the system.
Lateral movement. Testers navigate through the network to access additional systems and sensitive information, simulating how an attacker might expand their foothold within the environment.

Post-Exploitation

Following successful exploitation, testers assess the extent of their access and the potential impact on the organization. This stage helps in understanding the risks associated with a security breach. Key activities during this stage include:

Data exfiltration simulation. Demonstrating the ability to extract sensitive data, such as customer information, intellectual property, or financial records.
Persistence techniques. Establishing methods to maintain access over time, such as installing backdoors or creating unauthorized user accounts.
Impact analysis. Evaluating the potential damage in terms of data loss, operational disruption, financial costs, and reputational harm to the organization.

Reporting

Comprehensive reporting is essential to communicate the penetration test findings to stakeholders effectively. The report provides a detailed account of vulnerabilities discovered and recommendations for remediation. Key components of the reporting phase include:

Detailed documentation. Providing a thorough description of testing activities, including methodologies, tools used, and step-by-step accounts of how vulnerabilities were exploited.
Risk assessment. Categorizing vulnerabilities based on their severity, likelihood of exploitation, and potential impact on the organization.
Recommendations. Offering actionable guidance for addressing each identified vulnerability, including technical solutions and suggestions for improving security policies and procedures.

Remediation and Retesting

After receiving the penetration test report, the organization addresses the identified vulnerabilities. Remediation and retesting ensure that security weaknesses are effectively mitigated. This stage involves:

Implementing fixes. Taking corrective actions such as applying patches, updating software, reconfiguring systems, or enhancing security controls.
Verification testing. Retesting the systems to confirm that the vulnerabilities have been successfully addressed and no new issues have been introduced.
Continuous improvement. Incorporating lessons learned into the organization's security strategy, updating policies, and planning for future security assessments to maintain a strong security posture.

Penetration Testing Tools

Below is a list of essential tools commonly used in penetration testing:

Nmap (Network Mapper). A tool for network discovery and security auditing; used to scan networks and identify open ports and services.
Metasploit Framework. An open-source platform for developing and executing exploit code against target systems.
Wireshark. A network protocol analyzer that captures and displays packets in real time, useful for network troubleshooting and analysis.
Burp Suite. An integrated platform for performing web application security testing, including intercepting proxy, scanner, and intruder tools.
John the Ripper. A fast password cracker that supports various encryption technologies, used to detect weak passwords.
Nikto. A web server scanner that tests for dangerous files, outdated versions, and server misconfigurations.
SQLmap. An automated tool for detecting and exploiting SQL injection vulnerabilities in web applications.
Aircrack-ng. A suite of tools for assessing Wi-Fi network security, including monitoring, attacking, testing, and cracking.
Hydra. A parallelized network login cracker supporting numerous protocols; used for brute-force password attacks.
OWASP ZAP (Zed Attack Proxy). A tool designed to find vulnerabilities in web applications; particularly useful for beginners.
Nessus. A comprehensive vulnerability scanner that identifies vulnerabilities in operating systems, applications, and devices.
Maltego. An open-source intelligence and forensics application that provides data mining and link analysis.
Kali Linux. A Linux distribution designed for penetration testing and digital forensics; preloaded with numerous security tools.
Canary Tokens. A system for detecting unauthorized activities through the use of decoy tokens.
Social Engineering Toolkit (SET). A framework for simulating social engineering attacks, including phishing campaigns.

Read our article on the best penetration testing tools for an in-depth exploration of the latest security software on the market.

Advantages and Disadvantages of Penetration Testing

You must understand both the benefits and limitations of penetration testing to make informed decisions about its implementation.

Advantages of Penetration Testing

Penetration testing offers the following benefits:

Simulates real-world attacks. Penetration testing models real-world attacks, demonstrating how actual threats would impact the organization. For example, a tester might discover a flaw in a web application that allows unauthorized access to confidential customer data, highlighting a critical issue that needs immediate attention.
Uncovers complex security flaws. Penetration testing uncovers complex security flaws that automated tools might miss. For instance, a penetration tester might find that combining two minor vulnerabilities enables them to bypass authentication entirely, revealing a serious security gap.

Disadvantages of Penetration Testing

Penetration testing presents the following challenges and risks:

Limited scope. Penetration tests often focus on specific areas, potentially missing vulnerabilities outside the defined scope. For example, concentrating only on network security might lead to overlooking weaknesses in web applications or mobile platforms.
Time-consuming and costly. Penetration testing is time-intensive and requires skilled professionals and extensive resources, making it more expensive than automated methods like vulnerability scanning.
Risk of system disruption. If mishandled, penetration testing causes outages or data loss. For example, testing certain exploits can crash a server, interrupting business continuity if you do not have a backup.

Penetration Testing FAQ

Below are answers to frequently asked questions about penetration testing.

Who Performs Penetration Testing?

Penetration testing is conducted by skilled professionals known as penetration testers or ethical hackers. These individuals possess in-depth knowledge of cybersecurity principles, attack methodologies, and defensive strategies. They are proficient in various programming languages, networking concepts, and security tools.

Penetration testers often hold industry-recognized certifications, such as:

Certified Ethical Hacker (CEH).
Offensive Security Certified Professional (OSCP).
Certified Information Systems Security Professional (CISSP).
GIAC Penetration Tester (GPEN).
CompTIA PenTest+.

Organizations may employ in-house penetration testers or engage external security firms specializing in penetration testing services.

Can I Do My Own Penetration Testing?

While organizations can perform basic security assessments internally, conducting comprehensive penetration testing requires specialized skills and experience. Attempting to perform penetration testing without proper expertise often leads to incomplete assessments or unintended consequences, such as system disruptions, downtime, or legal issues.

Employing professional penetration testers ensures that the testing is thorough, legally compliant, and aligned with cybersecurity best practices. External testers also provide an objective evaluation and may identify vulnerabilities that internal teams might overlook due to familiarity or bias.

When to Perform Penetration Testing?

You should conduct penetration testing proactively to identify and address vulnerabilities before attackers exploit them. Key scenarios when penetration testing is essential include:

Before a system goes live. Testing new systems, applications, or networks before deployment ensures that vulnerabilities are identified and remediated early.
After significant changes. Performing tests following major updates, upgrades, or configuration changes to systems and infrastructure verifies that security controls remain effective.
In response to emerging threats. Testing strengthens defenses against new attack techniques or vulnerabilities that have been disclosed publicly.
To meet compliance requirements. Regular penetration testing is mandated by industry regulations such as PCI DSS, HIPAA, or GDPR.
Following a security incident. Conducting tests after a breach or attempted attack helps assess the extent of vulnerabilities and prevent future incidents.

How Often Do You Perform Penetration Testing?

The frequency of penetration testing depends on factors such as the organization's size, industry, regulatory obligations, and risk tolerance. General recommendations include:

Annually. At a minimum, organizations should perform comprehensive penetration testing once a year.
Biannually or quarterly. High-risk environments like those managing sensitive financial or personal data require more frequent testing.
After significant changes. Conduct tests whenever there are substantial modifications to the infrastructure, applications, or security policies.
Continuous testing. Implementing ongoing security assessments and vulnerability scanning to maintain an up-to-date understanding of the security posture.

Does Penetration Testing Require Coding?

Penetration testing frequently demands proficiency in coding and scripting languages, enabling testers to develop custom exploits by creating or modifying code to target specific vulnerabilities. Coding skills are also essential for automating repetitive tasks, such as scanning and data collection, making the testing process more efficient. Additionally, penetration testers analyze source code to identify security flaws within applications, while understanding application behavior allows them to interpret software functions and uncover logical vulnerabilities.

Proficiency in languages such as Python, Ruby, Perl, JavaScript, or C/C++ enhances a tester's ability to conduct comprehensive assessments.

Is Penetration Testing Legal?

Penetration testing is legal only when conducted with explicit authorization from system owners, with legal considerations including written consent that outlines the scope and limitations of testing activities. Testers must also comply with local, national, and international data protection regulations to ensure adherence to legal standards. Additionally, ethical conduct is paramount, as testers are expected to follow professional codes of conduct, avoiding unauthorized access or harm to systems and data beyond the agreed-upon scope.

Unauthorized penetration testing, or hacking without consent, is illegal and results in criminal charges and civil lawsuits.

What Happens After Penetration Testing?

After a penetration test, organizations must take the following steps:

Review the penetration test report. The first step is to review the report provided by the testers. This report outlines the discovered vulnerabilities and offers specific recommendations for remediation.
Prioritize vulnerabilities. This should be performed based on the severity and likelihood of exploitation. The process involves assigning responsibilities to specific team members, setting clear deadlines for each remediation task, and allocating the necessary resources to address the identified issues.
Implement security enhancements. This may include applying software patches, updating system configurations, strengthening Identity and Access Management (IAM), and improving network segmentation to limit the impact of potential breaches.
Update policies and procedures. Organizations must also update their IT security policies and procedures based on the insights gained from the test. Implementing security awareness training helps prevent future vulnerabilities.
Retest and monitor continuously. Retesting is vital to verify that the vulnerabilities have been effectively resolved. Continuous monitoring and regular security assessments help maintain a strong security posture and adapt to evolving threats.

How Much Does Penetration Testing Cost?

The cost of penetration testing varies based on the following factors:

Scope and complexity. Larger networks and complex systems require more time and effort to assess.
Depth of testing. Comprehensive tests that include social engineering, physical security, and in-depth code analysis are more expensive.
Expertise of testers. Highly qualified and experienced testers charge higher fees.
Type of testing. Specialized tests, such as wireless or cloud penetration testing incur additional costs.
Geographic location. Costs vary widely based on regional market rates and the availability of qualified professionals.

On average, penetration testing services range from $4,000 to over $100,000. Small businesses might spend around $4,000 to $20,000, while larger companies could incur costs exceeding $50,000 for extensive assessments.

Read our article on IT cost reduction to learn how to optimize your IT budget without causing turmoil.

Strengthening Cybersecurity with Penetration Testing

Penetration testing is a vital component of strengthening an organization's cybersecurity defenses. Simulating real-world attacks uncovers hidden vulnerabilities that malicious actors could exploit. Despite its limitations, such as higher costs and the potential for system disruptions, the insights gained from penetration testing are precious.

In an era of increasingly sophisticated and common cyber attacks, penetration testing is essential for any organization committed to maintaining strong cybersecurity.

Rack Density Increasing: Trends and Implications

Average rack density (the amount of power the equipment within a rack uses) has gradually been increasing over the past decade. While the rise was slow and steady, IT advancements are now rapidly pushing the average rack density up and threatening to disrupt traditional practices in data centers.

This article explains the reasons behind the recent increase in density per rack. Read on to learn what is causing this trend and what steps data centers will have to make to remain competitive.

A Steady Rise in Rack Density

For years, data centers housed equipment in racks that required 2 to 5kW of power on average. These setups were easy to accommodate with single-phase power and blown-in air for cooling.

However, recent IT advancements are increasing the demand for compute-intensive workloads that require more power. Some of these rapidly developing technologies are:

Cloud services.
Virtualization and containerization.
Artificial intelligence (AI), machine learning (ML), and deep learning workloads.
Internet of Things (IoT).
Blockchains and cryptocurrencies.
5G networks.
Edge computing.

These technologies require high processing power and high-density racks that go beyond the traditional 2 to 5kW average. As a result, enterprise and on-premises data centers are increasing the average density, a concern that was once unique to high-performance computing servers and hyperscale centers. As data centers look to meet the growing demand, the average server rack (computer rack) density jumped to 8.4 kW per rack in 2020, up from 7.3 kW in 2019. Analytics and surveys run by the Uptime Institute report the following split:

46% of data centers have server rack densities in the 5 to 9kW range.
25% operate within the 1 to 4kW average range.
13% have rack densities between 10 and 19 kW/rack.
16% report 20kW or higher averages.

A closer look at the top 16% reveals the following densities:

Over 6% of facilities have an average of 20 to 29kW/rack.
5% operates in the 30-39kW range.
Just under 5% have 40 kW and higher densities.

As an additional indicator of the trend, over 45% of data centers expect their average rack density to be over 11 kW in the near future.

Our article on data center power infrastructure provides an in-depth look at how data center facilities manage power consumption.

What Is Driving the Rise in Rack Density?

Data consumption is on the rise as the use of cloud services increases and new technologies gain ground. The demand for compute processing is also growing as companies increasingly rely on power-hungry workloads such as:

BI reporting and dashboards.
Custom, ad hoc, and self-service analytics.
Hybrid operational and analytic processing (real-time advising, real-time operational analytics, etc.).
Transactional operational workloads (CRM, ERP, financial transactions, sales order entries, etc.).
AI/ML and advanced analytics.
Data science and discovery.
Batch processing and HPC.

AI and ML are at the top of the causes behind the recent increase in average density. While AI and ML are still in relatively early stages, they already require high amounts of power.

AI/ML systems ingest large datasets, learn from them, and draw conclusions when they get new data. These systems require high levels of processing and are often coupled tightly with a single shared memory pool. As a result, 1kW/rack unit is the current standard for AI servers. A rack can have over 30 units, so setups typically run in the 20 to 40kW per rack range. Similarly, it is not uncommon for HPC deployments to reach the 50kW/rack mark.

Alongside AI data-crunching, there is an increase in advanced modeling and data analytics, which is another factor causing the need for higher-density racks. Other drivers of the trend are:

Power-hungry government and military-grade apps.
Demand for high-definition videos.
Augmented and virtual realities.
Intelligent transportation systems.
The e-sports industry that requires high-performance and low latency.

PhoenixNAP's flagship data center in Phoenix, AZ provides a high-density design, 100% environment stability, SOC type-2 compliance and HIPAA-ready colocation services.

IT Equipment Is Also Pushing Rack Density Up

In response to the growing demand for processing power, manufacturers are increaseing the power of chips.

In the early to mid-2010s, mainstream server processors used under 100W, while dual-processor servers consumed about 200W at full load. Nowadays, Intel server chips (which are in more than 90% of server processors) go past the 200W barrier and bring the total average server consumption close to 500W.

Specialized chips are becoming even more power-hungry due to complex analytics and multimedia elements. The new types of chips must support capabilities such as:

High parallelism (with potentially thousands of cores operating at the same time).
Solving memory bandwidth problems to process large amounts of data (typically achieved by running a high number of operations in proximity).
Variable precision (using different quantities of digits in calculations).

These accelerator chips include graphics processing units (GPUs), field-programmable grid arrays (FPGAs), and custom application-specific integrated circuits (ASICs). These components require higher energy per chip than a standard CPU. For example, a GPU card can now draw about 300W, so each setup with three cards per rack unit draws as much as 1kW/rack unit.

A Challenge for Legacy Data Centers

Besides the increase in power-hungry processes, modern data centers are also accelerating the move towards higher rack density. Colocation providers see the trend as an opportunity to optimize resources as raising the density bar allows a data center to:

Maximize the use of cooling by having more computing power in each cabinet.
Free up square footage for other equipment (or reduce the overall size of the data center).
Combine virtual machines onto fewer dedicated servers and reduce the number of necessary cabinets and pieces of equipment.
Expand vertically instead of horizontally (up instead of out) and facilitate more users in the same space they already possess.

While some data centers embrace the change, some facilities will be under pressure to improve cooling and deploy new technologies. Changing operating strategies is a challenge, especially if a business with legacy equipment hopes to make a smooth, efficient transition at scale.

During the transition period, some data centers will likely offer higher density as an added service. This strategy will help offset the costs and provide extra time to remodel the equipment.

Read about data center security and learn what measures colocation providers deploy to ensure their facilities remain safe.

Key Business Planning Takeaways

Current rack density averages do not suggest an immediate need for technical overhauls, but data centers should prepare for evolving consumer demands. Here are the key factors to consider during business planning:

Customer expectations are changing. As companies develop software that requires more power, high density will eventually become the industry norm.
High density per rack will become an even more significant consideration for data center design and capacity planning.
AI and ML are at the core of digital transformation as companies look to use data better and improve decision-making. As AI/ML systems are most effective when running in proximity, businesses will have much higher needs for energy delivery per rack.
Standard air-cooling systems (heat sinks and fans) cannot efficiently control the temperature of the latest chips or large numbers of racks with AI/ML workloads. Facilities that offer direct liquid cooling (DLC) will house new chips more efficiently as this strategy can efficiently cool 50-100kW per cabinet.
As the number of stacked equipment increases, racks will become heavier. Some facilities will require new racks with larger load ratings or floors that can withstand heavier loads.
Chips are becoming more energy-hungry. This trend, along with the increase in the use of accelerator chips, will make the shift towards higher-density racks a natural choice when updating equipment.
Cloud providers will have to adapt their operating strategies to deal with higher-density requirements.
Some enterprises will no longer be able to support the required densities in an on-premises data center. There will be a rise in colocation hosting as setting up equipment in a third-party provider's facility becomes an even more sensible decision.

Our article on colocation pricing explains how facilities calculate prices and helps better understand your data center bills.

Average Rack Density Is Going Nowhere but Up

We expect rack densities to keep rising as data centers look to meet new customer demands.

The change will not happen overnight, but the industry is currently evolving in ways that favor higher-density racks. While there is still no immediate pressure, forward-thinking organizations are planning accordingly.

Software Testing Methodologies and Models

Perfect software does not exist, and every program has potential failure points. Software testing is a software development lifecycle stage during which the team discovers unwanted errors in a program or system.

Different testing methodologies help pinpoint several types of software errors. Knowing how each software testing model works is essential to building, deploying, and maintaining a high-quality testing strategy and software.

software testing methodologies and models.

Why Is Testing Important in SDLC?

The testing phase is a critical stage in the software development lifecycle. It comes after software implementation, and testing aims to discover and fix software errors.

Software testing is crucial because the product goes into production after testing. Every software development team must deliver quality software for two reasons:

The end user benefits from having a high-quality product built according to requests and specifications.
The development team's integrity and the possibility of new projects depend on the software’s quality.

A software testing team performs various checks for different issues separately from developers. This approach aids in having a team solely focused on discovering problems and allows for implementing continual development.

Software Testing Methodologies

Software testing is a formal process performed by a testing team to help confirm a program is logically correct and valuable. Testing requires using specific test procedures and creating test cases.

Software testing is performed in two stages:

Verification - Is the system built correctly?
Validation - Is the system constructed according to the user requirements?

Software testing uses several methodologies and models to answer these two questions.

Black Box Testing

In the black box testing methodology, a program is a closed (black) box with unknown details. The only visible components to a tester are the program inputs and outputs.

A tester can determine whether a program is functional by observing the resulting outputs for various inputs. Black box testing does not consider a program’s specifications or code, only the expected behavior for different test cases. Black box testers do not necessarily have to be very skilled since they do not interact with any code.

Black box testing comes with both benefits and drawbacks. The critical advantage of black box testing is there is no requirement to work with code and programming logic. However, testing for all input combinations is impossible.

Three types of tests are based on the black box testing methodology: functional testing, non-functional testing, and regression testing.

Functional Testing

Functional testing checks whether the software performs a specific function without considering which component within the system is responsible for the operation.

The testing team checks functionalities for both good and bad inputs. An example function is a user login page behavior. A functional test checks whether a user can log in with the correct credentials or not log in with incorrect credentials.

As the software’s complexity increases, so does the number of functions within the software. The order of testing functions is crucial for an efficient and functional testing strategy. As functionalities are often nested, the behavior of the software depends on the order of steps a person takes when using the software.

The main benefit of functional testing is that the testing team can check individual functionalities before all software components are completed. The probability of detecting errors in functional testing is exceptionally high, as it shows problems when using software from a user's perspective.

Non-Functional Testing

The non-functional testing method verifies software aspects apart from functionalities and features. This testing method focuses on how a program performs certain actions under specific conditions.

Non-functional testing helps uncover if a program is usable from a user's perspective. The method checks for usability issues, such as cross-compatibility across different devices, browsers, or operating systems.

Regression Testing

Regression testing is a software testing method that ensures all new code changes do not cause issues in previously tested components and negatively affect system stability. The testing method repeats tests from previous iterations, ensuring that the latest changes do not cause unwanted effects on existing code.

Regression testing is necessary after any program action that results in changes to the original code, such as:

Implementing new requirements.
Adding new functionalities to the program.
Removing existing functionalities.
Fixing any defects.
Optimizing for better performance.

If software changes often, the best approach is to use automated testing tools and create reusable tests for multiple iterations and cycles of regression testing.

White Box Testing

The white box testing method is the opposite of black box testing. In this method, the program is an open (white) box whose inner workings are known to the tester.

White box testing analyzes the source code and requires the following skillset:

Coding and scripting knowledge.
Familiarity with the specific programming language in use.
The design of particular components.

Testers form a plan based on the program's structure. For example, white box testing can include creating scripted tests that go through the whole code and run every function. Specific tests can check whether there are infinite loops or cases where the code does not run.

The main drawback of white box testing is the number of test iterations, which increases as the application becomes more complex. The method requires creating a strategy where recursions or loops execute fewer times for carefully chosen and representative values.

Three types of tests are based on the white box testing methodology: statement testing, path testing, and branch testing.

Statement Testing

Statement testing is a testing technique within white box testing. The method assesses all executable statements in the code at least once. For example, if a code block contains several conditional statements, the statement testing technique involves going through all input iterations to ensure all parts of the code execute.

The statement testing technique discovers unused parts of code, missing referenced statements, and leftover code from previous revisions. As a result, statement testing helps clean up the existing code and reduces redundant code or adds missing components.

Path Testing

Path testing creates independent linear paths throughout the code. The testing team creates a control flow diagram of the code, which aids in designing unit tests to evaluate all code paths.

Analyzing different paths helps discover an application’s inefficient, broken, or redundant flows.

Branch Testing

Branch testing maps conditional statements in the code and identifies the branches for unit testing. The branch types are:

Conditional: Executes when a condition is fulfilled.
Unconditional: Executes regardless of any circumstances.

For example, the following code contains several nested statements:

if condition 1:
    W
else if condition 2:
    X
    Y
else:
    Z

A tester identifies all conditional branches. In the example code, conditional branches are W, X, and Z because the statements only run under a specific condition. On the other hand, Y is an unconditional branch because it always executes after the X statement.

Branch testing aims to execute as many branches as possible and test for all branching conditions.

Note: Check out our article on Black Box Testing vs. White Box Testing to learn more about the differences between these two testing methodologies.

Functional Testing

Functional testing is a subtype of black box testing that considers the software specifications for a given function or program. The testing method provides various inputs and analyzes the outputs without considering the internal software structure.

Functional testing involves 4 distinct steps that start from more minor parts of the code and branch out into evaluating the entire system. The model aims to analyze a component or the program's compliance and check whether a system does what it is supposed to do according to specifications.

Step 1: Unit Testing

Unit testing is a software testing methodology that validates individual parts of source code. A unit test helps determine the functionality of a particular module (unit), and the process isolates individual parts to decide whether they function correctly.

A unit is an individual function, procedure, or object-oriented programming class. Typically, unit testing helps validate the front-end interface.

The main benefit of unit testing is early problem discovery in the development lifecycle. In test-driven development, such as scrum or extreme programming, testers create unit tests before any code exists.

The main drawback when applying unit testing is the need to evaluate complex execution paths in a program. Unit tests are localized and incompatible for discovering integration or system-wide errors.

Step 2: Integration Testing

Integration testing is a phase that comes after unit testing. The method combines previously assessed individual program units (modules) into larger groups and performs tests on the aggregates.

There are several different approaches to integration testing:

The bottom-up strategy first evaluates and integrates low-level components before moving to more complex groups.
The top-down approach uses reverse engineering to assess components from more complex groups and simplifies them into smaller units.
Sandwich (hybrid) testing combines the bottom-up and top-down strategy by simultaneously testing low and high-level components.
Big Bang testing combines all components into a single large unit for testing. The method does not have an incremental approach compared to other testing methods.

Integration testing validates the connections between the front-end interface and an application's back end.

Step 3: System Testing

System testing performs tests on a completely integrated system. The step analyzes the behavior and compares it to expected requirements (Quality Assurance), validating a fully integrated software.

System testing aims to discover issues missed by integration and unit testing and to provide a comprehensive overview of whether the software is ready for release. The different testing approaches in system testing consider how well the software works in various environments and how usable the software is.

The main challenge of system testing is designing a strategy that fits within the available time and resource constraints while providing a comprehensive analysis of the entire system after integration.

Note: To easily scale out for testing purposes, we recommend using Kubernetes on BMC. It provides on-demand production-ready cloud-native environments.

Step 4: Acceptance Testing

The final part of functional testing is the acceptance test. The testing method aims to assess the approval of the application's end-user. The approach engages end-users in the testing process, gathering user feedback to identify potential usability issues or errors that may have been missed during previous testing phases.

Acceptance testing falls into one of the two following categories:

User acceptance testing allows target users to evaluate and use the software through beta testing or a similar strategy. The user base determines whether the software operates as expected.
Operational acceptance testing checks whether the software is functional and operates as expected. The test examines various software components, such as security, backup and disaster recovery, and failover testing.

After acceptance testing, the software is ready for production if the results meet the acceptance criteria. Otherwise, the software gets pushed back into previous development and testing phases if the testing does not pass the threshold.

Non-Functional Testing

Non-functional testing evaluates the software from the users’ perspective, focusing on the user experience. The testing methodology aims to catch issues unrelated to a software's functionality but essential to the user's experience.

Non-functional testing considers parameters such as:

Security
Reliability
Scalability
Usability
Availability

The focus of non-functional testing is on how a product operates rather than how it behaves in specific use cases. This testing model is conducted through performance testing, security testing, usability testing, and compatibility testing.

Performance Testing

Performance testing checks the speed, scalability, and stability of the software. Several different performance testing subtypes exist, such as:

Load tests check how the software functions under regular user demand.
Stress tests examine how software behaves under a high user load or other complex circumstances.
Spike tests check how software behaves under sudden high user load spikes.
Endurance tests show an application's stability over an extended time.

All performance tests aim to catch and fix low latency and performance problems that degrade a user's experience.

Security Testing

Security testing checks for any security issues in software and is one of the most critical software testing methodologies. The method checks for any vulnerabilities within the system and possibilities of cyber attacks.

Methods such as penetration testing and vulnerability scanning help discover and lower security risks within the software, and there are also numerous penetration testing tools to automate the testing process.

Usability Testing

Usability testing evaluates how user-friendly and convenient software is to a user. The tests highlight how quickly an unguided user can do something in the program or application. The usability test results show how quickly a new user can learn to use the software and whether any interface improvements are necessary.

Compatibility Testing

Compatibility testing shows a system's behavior in various environments and with other software. The method focuses on integration with existing solutions and technologies.

Software Testing Models

The testing phase in the software development lifecycle is not the only place where errors can be identified and fixed. All development stages benefit from including software tests.

Continuous software development also requires continuous software testing. Software development should work with the testing team to discover potential problems early on or to determine places where testing is impossible. Early discovery is better, and as the steps progress, the cost of finding and fixing errors increases. According to the IBM System Science Institute, the relative cost of discovering and repairing defects in the maintenance phase is around six times higher.

Therefore, it is crucial to see how testing integrates into various software development processes and methodologies. Below is an overview of well-known software development models and how testing integrates into each method.

Note: Learn more about about continous software development, integration and testing in our article on CI/CD.

Waterfall Model

The waterfall model is a software development method divided into sequential steps or stages. The team progresses to the following stage only after finishing the previous phase.

The testing team starts creating a test plan and strategy during the requirements phase in the waterfall model. Once the software goes through the implementation phase, testers verify if the software works correctly and according to specifications.

The main benefit of the waterfall method in software testing is that the requirements are well-defined and easily applied in the testing phase. The model is unsuitable for projects with frequently changing conditions and unplanned events.

Advantages

Simple. A high level of abstraction simplifies the communication process with the end user, who does not need to know the technical process details to participate in the development process.
Easy to follow. Project managers have access to critical points in project development, which allows them to be aware of the level of progress during development.
Easy to apply. The model goes through a single iteration, which is convenient for replacing existing solutions with new software.

Disadvantages

No feedback loop. The waterfall model lacks a feedback mechanism and multiple iterations. Defining all requirements at the start of the project is impossible, and returning to any previous step to make changes is not a supported path in the model.
No apparent connection between phases. The model assumes that every previous development phase is the input for the following step. However, the model does not define how requirements transform into a design.
Not focused on problem-solving. The waterfall model approaches software design as a hardware or production mechanism. On the other hand, software design requires testing and trying different approaches. The model limits any modular and creative process during software development.
Limited end-user interaction. The waterfall model communicates with a user only in the first step of the development phase and in the final stage when the product is complete. The approach limits user interaction, which makes the development process inefficient.

V Model

The V model is an extension and improvement of the waterfall model. The model is divided into sequential steps, with additional testing steps for each development phase. The V model goes through all the stages in functional testing to verify and validate the software.

The shape of the V model shows the corresponding test phases to the development life cycle phases. When viewed left to right, the model demonstrates the order of steps as time progresses, while viewing from top to bottom reveals the abstraction level.

Advantages

Feedback mechanism. The V model is practical for simple and complex projects due to the possibility of returning to any of the previous phases.
Verifies and validates. The model checks whether a project is developed well, fully implemented, and fulfills user requirements.
High-quality products. The development process is well-organized and controlled, guaranteeing the software's quality.
Testing in early phases. The testing team participates in the early development phases, which results in a better understanding of the project from the start. The model significantly saves time and resources on testing in the later stages of development.

Disadvantages

Not flexible. When problems appear, the team must update all phases of the software development process, including documentation. Any change slows down the SDLC.
Costly. Implementing the V model requires significant resources to support numerous development teams. The model is better suited for larger projects and businesses.

Agile Model

The agile methodology is a fast and iterative approach to developing software that follows the principles defined in the Agile Manifesto. It breaks down software development into small increments and multiple iterations. The agile model allows constant interaction with end users, and requirements change constantly.

Testing in the agile model happens in every iteration. Software testing in this environment requires continual testing throughout the CI/CD pipeline via automated testing tools and frameworks.

Advantages

Fast development. Deploying software happens quicker than in other models. The model is adaptive and responsive to changes, resulting in shorter turnaround times.
High level of user interaction. Constant end-user feedback ensures acceptance testing happens often. As a result, the product is closer to the requirements with each iteration.
Smaller iterations. Errors and defects are easier to spot and analyze in smaller chunks. Delivering software takes less time, and new iterations happen often.

Disadvantages

Unpredictable. Although the testing team gathers user feedback, there is no guarantee the next iteration will contain these changes. The fast pace creates an unpredictable product roadmap.
Phase overlap. Every iteration goes through all the development phases. As sprints progress, it becomes trickier to distinguish who is responsible for which task.
Costly. Continual releases to production and development result in higher expenses. Predicting the necessary effort for each change becomes difficult.

Scrum Model

The scrum model is a project management approach that uses principles from the agile model. The model is goal-oriented and time-constrained into iterations known as sprints. Every sprint consists of meetings, milestones, and events managed by a scrum master.

The scrum model does not feature a testing team, and developers are responsible for constructing and implementing unit tests. The software is also often tested by the end user in each sprint.

Some scrum teams do feature testers, in which case testers must provide time estimations for every testing session during scrum meetings.

Advantages

Fast-paced. The scrum model ensures project delivery happens quickly, which makes it suitable for fast-paced projects under development.
Cost efficient. Every sprint consists of several members, and the fast-paced environment ensures the project completion happens within a specified time frame.
High-level of user interaction. Like the Agile model, scrum releases code often, which results in constant user interaction. Continual feedback results in satisfying user requirements through every sprint.

Disadvantages

High chance of failure. The scrum model requires a high level of interaction and commitment. Daily meetings are stressful, and one team member leaving impacts the whole project. The software is at a higher risk of failure in a non-compliant team.
Lacks quality. When a model lacks a testing team, the quality of the software is unacceptable. The resulting software is of lower grade than those undergoing intensive testing.

DevOps Model

The DevOps model combines continuous testing into every development stage, while also having a dedicated testing role in the team. The goal of testing in the DevOps pipeline focuses on software quality and risk assessment.

Automated testing and test-driven development improve code reliability, which helps minimize the likelihood of new builds breaking existing code.

Advantages

Fast software delivery. DevOps improves the delivery speed for new features and bug fixes. Quick response times to issues improve customer satisfaction with the product.
Error efficient. Integrating testing in the initial stages of development avoids having to fix problems later in the development cycle.
Quality software. Automated testing and continuous deployment improve software quality. DevOps ensures every change is tested before deploying software to production.

Disadvantages

Difficult to implement. DevOps requires integrating development with operations and following DevOps principles, which is hard to achieve in large organizations with complex systems.
Excessive costs. The model requires significant investments into infrastructure and DevOps tools. A misconfigured system creates integration challenges that are difficult to manage.
Increased risks. DevOps heavily relies on automation, which leads to problems if not configured properly. Issues are difficult to track down when they do occur.

Learn how to set up a test sandbox environment you can easily scale for production workloads.

Iterative Development

Iterative development divides software development steps into subsystems based on functions. The method is incremental, and each increment delivers a completed system. Every new iteration improves existing processes within every subsystem.

Early releases provide a primitive version of the software, while every following release improves the quality of the existing functionalities. Testing is simpler in early phases and increases in complexity as iterations progress.

Advantages

Risk control. Iterative development allows starting with high-risk tasks first. The controlled nature of the development method enables improving any issues through iterations.
Easy to follow. Every iteration shows an improvement from the previous. Measuring changes between iterations becomes simple for project managers.
Specialized versions. Versioning software becomes simple, as development teams can focus on specific improvements with each iteration. For example, one iteration improves the user interface, while the following iteration improves the software's performance. The approach simplifies the test design process.
Early training. Since iterative development delivers a simpler version of the full software, users are engaged and use the software early. User feedback is based on the whole system, and improvements are simpler to implement in the following iterations.

Disadvantages

Costly. Every iteration requires the presence of the whole software development team.
Lacks full specifications. The iterative development approach starts by creating a system from simple requirements. As time progresses, the conditions also change. The fluidity of specifications makes it challenging to see when the project ends.
Hard to manage. Iterative development requires intense project management and risk assessment for each iteration. As projects grow, the system's complexity increases.

Spiral Model

The spiral model is an agile model with a focus on risk assessment. The model combines the best qualities of the top-down and bottom-up development methods. The method uses the waterfall model phases as increasingly complex prototypes.

As risk analysis is the focus of every step, the spiral model enables the early discovery of faults and vulnerabilities. The model performs an early assessment of issues, which makes security testing less costly in the long run.

Advantages

Flexible. The spiral model is adaptable to requirement changes, even after developing a feature. There is less strain on the testing team when reporting issues.
High level of user interaction. The iterative approach in the spiral model allows engaging the customer in the development process and receiving continual feedback. The development team quickly makes changes during development, which saves costs and resources in the long run.
Secure. Risk analysis is in every step of the development process, and the iterative approach also helps manage risk. The spiral model focuses more on software security compared to other development models.

Disadvantages

Costly. The spiral model best suits large projects and complex software.
Specialized risk analysts. The model depends on having high-quality risk analysts included in every development step for efficient risk assessment.
Time management. The duration of each phase is unknown. The model is susceptible to exceeding the budget and falling behind on time constraints.
Complex. The model is hard to follow and requires an increased focus on protocols and documentation in every step of development.

Note: Learn more about the Automated Security Testing Best Practices.

RAD Model

The RAD (Rapid Application Development) model is an agile methodology that combines prototyping with iterative development. The method focuses on gathering user requirements, while the rest of the development process has no specific plan or steps.

RAD is a fast-paced technique that focuses on creating reusable components that serve as templates for future projects or prototypes. The testing team assesses all prototypes in every iteration and immediately integrates the components into the final product.

Advantages

Flexible. The model quickly implements requirements changes and accommodates new user requirements.
Reusable. Creating reusable prototypes provides a templating approach to development, increasing code reusability and requiring less effort in the long run.
Fast. The development time and short iterations enable continual software integration and rapid delivery. The RAD model incorporates various automation tools to speed up the development process.

Disadvantages

High expertise dependency. RAD-based teams are small, highly skilled, and technically strong. The RAD model requires an experienced team of developers to identify and model user requirements.
Only suitable for modular systems. Not all software has clear-cut components. RAD requires creating smaller prototypes that are reusable. Complex systems require specialized features which do not apply to all use cases.

Extreme Programming

Extreme programming (XP) is an agile method for developing software best suited for small to medium-sized teams (6-20 members). The technique focuses on test-driven development and short iterations that provide users with direct results.

XP has no strict methodology for the development team to follow. Instead, the method provides a flexible framework where procedures or the sequence of steps changes depending on the activity. The Agile Manifesto principles, and techniques like pair programming, are vital components in XP.

Advantages

Focus on software development. The team creates software rather than focusing on meetings and documentation. The work environment is comfortable for developers, with many opportunities to learn and improve skills.
Test-driven development. Unit tests exist before writing code, and programmers know what is tested before creating software. This approach stimulates programmers to take better precautions while coding.
Short development time. The lack of rules and procedures makes the software delivery speedy. Quick results are beneficial to customers.

Disadvantages

Hard to implement. The method is tough to implement. XP requires programmers with strict discipline who accept and follow the required principles and can work together closely.
Client dependent. The client chooses whether to participate in the development process. A non-participating client often leads to unsatisfactory software.

Conclusion

High-quality software testing is what differentiates between quality software and a lackluster project. The importance of software testing is crucial to development, which is why there are so many testing methodologies and approaches. Development teams should follow trends in software testing and be ready to fundamentally change their approach to profit from new software testing methodologies and models.

Next, check out how automated testing frameworks help streamline the testing process and improve testing speeds during the testing phase.

Hybrid Cloud Adoption Benefits and Strategies

Modern organizations must balance innovation and agility with security and control. Traditional IT infrastructures often struggle to meet these demands and accommodate rapidly changing workloads.

A hybrid cloud solves this issue by seamlessly integrating the public cloud's flexibility and scalability with the private cloud's security and control. By combining these two solutions, businesses can overcome their limitations and achieve optimal performance.

This article explores hybrid cloud adoption, outlines implementation strategies, and highlights the benefits of this method.

What Is Hybrid Cloud Adoption?

Hybrid cloud adoption involves integrating public and private cloud services with on-premises infrastructure to create a cohesive IT system. This approach enables organizations to distribute workloads across different environments based on their requirements for security, compliance, performance, and cost.

Components of Hybrid Cloud

The components below work together to create a hybrid environment.

Public cloud infrastructure. This component includes a range of cloud services offered by third-party providers, including Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). Key characteristics of the public cloud include elasticity, pay-per-use pricing, and a broad spectrum of services like compute, storage, networking, and databases.
Private cloud infrastructure. This component refers to IT resources dedicated to a single organization, often managed on-premises or in a colocation facility. Private clouds provide greater control, security, and compliance adherence than public clouds. Virtualization, containerization, and software-defined infrastructure are commonly used technologies with this type of infrastructure.
On-premises infrastructure. This component incorporates legacy systems, applications, and hardware residing within an organization's data centers. Although these components are essential for modernization, they remain critical for business operations and must be integrated into the hybrid cloud environment.
Hybrid cloud management platform. This software layer orchestrates and manages resources across disparate environments, ensuring seamless integration and workload mobility. Key functionalities include resource provisioning, monitoring, automation, and governance.
Data management and integration. This component focuses on data consistency, replication, and synchronization across various cloud environments. It includes data integration tools, data lakes, and data governance policies to ensure data accessibility and reliability.
Connectivity and networking. This component refers to the infrastructure that enables communication between public, private, and on-premises environments. Virtual private networks (VPNs), software-defined wide area networks (SD-WANs), and network virtualization technologies are crucial for establishing secure and efficient connections.
Security and compliance framework. A robust security architecture is essential for protecting sensitive data and meeting regulatory requirements. This component includes identity and access management, data encryption, threat detection, and compliance certifications.

Hybrid Cloud Adoption Strategies

Here is a detailed roadmap to guide your hybrid cloud adoption process:

1. Assess Current Infrastructure

Before starting your hybrid cloud journey, you must understand your existing IT infrastructure. This assessment will help identify strengths, weaknesses, and areas for improvement.

Inventory Existing Systems

The first step in assessing the current infrastructure is conducting a thorough inventory of existing systems.

Document all servers, storage devices, network equipment, and other hardware components.
Identify all applications, databases, and software tools in use.
Catalog all data assets, including their types, locations, and usage patterns.

Evaluate Performance

Identify areas for improvement.

Measure current system performance using metrics like response time, throughput, and availability.
Find performance bottlenecks.
Assess resource utilization to identify underused or overused components.

Identify Gaps

Determine where you need improvements and how a hybrid cloud strategy could address these gaps.

Identify vulnerabilities and areas where security can be improved.
Determine if current systems meet regulatory and compliance requirements.
Identify areas where current systems do not meet business needs.

2. Define Business Objectives

Aligning your hybrid cloud strategy with your organization's goals and objectives is crucial for driving overall success.

Engage Stakeholders

Engaging stakeholders is the first step in aligning your hybrid cloud strategy.

Determine the individuals and groups that will be impacted by the hybrid cloud adoption, including IT leadership, business unit heads, and end users.
Gather input on business objectives, technology needs, and concerns through interviews, workshops, and surveys.
Capture each stakeholder group's needs and expectations to ensure they are addressed in the hybrid cloud strategy.

Define Risk Tolerance

Defining your organization's risk tolerance is essential for balancing the benefits of hybrid cloud adoption with potential risks.

Identify risks associated with cloud adoption, such as data breaches, compliance violations, and service outages.
Establish clear, acceptable levels of risk in areas like security, compliance, and financial impact.
Develop strategies to mitigate identified risks, such as deploying additional security controls or selecting cloud providers with strong compliance support.
Continuously monitor risk levels and adjust the cloud strategy to maintain alignment with the organization's risk tolerance.

Prioritize Needs

Focus on the most critical aspects of hybrid cloud adoption that will deliver the greatest business impact.

Determine the primary drivers for hybrid cloud adoption, such as the need for scalability, cost reduction, or improved performance.
Apply a structured framework, such as MoSCoW (Must have, Should have, Could have, and Won't have), to prioritize needs based on their impact on business outcomes.
Direct resources towards the highest-priority initiatives, ensuring that critical areas like security, compliance, and performance receive adequate attention.
Evaluate immediate needs and long-term strategic goals to ensure the hybrid cloud strategy addresses both effectively.

3. Conduct a Cost-Benefit Analysis

A thorough cost-benefit analysis will help justify the investment and ensure the benefits outweigh the costs.

Calculate Costs

Estimate the costs associated with migrating workloads to the hybrid cloud environment.

Calculate ongoing operational costs, including cloud service fees, maintenance, and support.
Identify potential hidden costs, such as data transfer fees, integration costs, and training expenses.

Identify Benefits

Quantify potential cost savings from reduced capital expenditure, improved efficiency, and optimized resource utilization.

Estimate performance improvements, such as faster response times, increased availability, and enhanced scalability.
Identify strategic benefits, such as improved agility, innovation, and competitive advantage.

4. Develop a Migration Plan

A well-structured migration should outline the steps, timelines, and resources required for the migration.

Phased Approach

Breaking down the process into manageable stages to minimizes disruption and ensures a smooth transition.

Start with a pilot phase to test the hybrid cloud environment with a small set of workloads.
Plan iterative migration phases to gradually move workloads to the hybrid cloud environment.
Develop a rollback plan to revert to the previous environment if issues arise.

Pilot Projects

Test the hybrid cloud environment and gather insights to inform subsequent migration phases.

Choose pilot projects based on their potential impact, complexity, and risk.
Document lessons learned from pilot projects to inform subsequent migration phases.
Define success metrics to evaluate the performance and effectiveness of pilot projects.

5. Ensure Security and Compliance

Security and compliance are critical to any hybrid cloud adoption strategy.

Data Protection

Implement robust security measures to protect sensitive information and ensure only authorized users can access data.

Implement encryption for data at rest and in transit to protect sensitive information.
Establish robust access control mechanisms to ensure only authorized users can access data.
Deploy intrusion detection systems to monitor and respond to security threats.

Compliance Requirements

Ensure the hybrid cloud environment meets all relevant regulatory requirements, such as GDPR, HIPAA, or PCI-DSS.

Implement audit trails to track and document all data access and management activities.
Obtain necessary compliance certifications to demonstrate adherence to industry standards.

6. Implement a Governance Framework

A robust governance framework should include policies, procedures, and controls to ensure compliance with security and regulatory requirements.

Policy Development

Create policies for data management, access control, and incident response to ensure the security and compliance of the hybrid cloud.

Develop policies for data classification, storage, retention, and disposal.
Establish policies for user authentication, authorization, and access control.
Define policies for incident detection, response, and recovery.

Monitoring and Auditing

Implement tools and processes to continuously monitor the hybrid cloud environment for security threats and performance issues.

Implement continuous monitoring tools to detect and respond to security threats and performance issues.
Generate audit reports to document compliance status and identify areas for improvement.
Conduct regular audits to assess compliance with governance policies and regulatory requirements.

7. Select the Right Cloud Providers

The right cloud providers must meet the organization's technical, security, and compliance requirements.

Vendor Evaluation

Assess potential cloud providers based on their capabilities, security measures, and service level agreements.

Evaluate potential cloud providers based on their technical capabilities, service offerings, and innovation roadmaps.
Assess the security measures and certifications of potential cloud providers.
Review service level agreements (SLAs) to ensure they meet the organization's performance and availability requirements.

Integration

The selected cloud providers should seamlessly integrate with existing systems and applications.

Verify that cloud providers offer robust API support for integration and automation.
Consider a multi-cloud strategy to leverage the strengths of multiple cloud providers and avoid vendor lock-in.

phoenixNAP’s hybrid cloud solutions deliver scalable infrastructure, top-tier security, and seamless integration. With robust SLAs and extensive API support, we are the perfect partner for your cloud journey. Contact us to learn more.

8. Train and Educate Staff

Ensuring staff members have the necessary skills and knowledge to manage the hybrid cloud is crucial for its success.

Skills Development

Encourage staff to enhance their skills in hybrid cloud technologies.

Develop training programs to educate IT staff on hybrid cloud technologies, best practices, and management tools.
Encourage staff to obtain relevant certifications to enhance their skills and knowledge.

Change Management

Change management involves the following steps:

Develop a change management plan to facilitate the transition to the hybrid cloud environment.
Address resistance to change by involving stakeholders in the planning process and addressing their concerns.
Communicate the benefits and implications of the hybrid cloud strategy to all stakeholders.

9. Optimize Workload Placement

Placing workloads in the most suitable environment enhances performance, security, and cost-efficiency.

Workload Analysis

Assess workloads to determine their requirements and place them in the most suitable environment.

Analyze workload performance to determine their response time, throughput, and availability.
Assess the security requirements of workloads to place them in an appropriately secure environment.
Evaluate the compliance requirements of workloads to ensure that they meet regulatory standards.

Performance Monitoring

Implement monitoring tools to continuously track the performance of workloads across the hybrid cloud environment.

Define performance metrics to measure the effectiveness of workload placement.
Adjust workload placement based on performance data and changing business needs.

10. Implement Disaster Recovery and Business Continuity

Disaster recovery and business continuity solutions are essential for the availability and resilience of the hybrid cloud. You should design them to minimize downtime and data loss in the event of a failure.

Backup Solutions

Develop a backup strategy to ensure that all critical data is regularly backed up.

Implement backup tools to automate the backup process and ensure data integrity.
Conduct regular backup testing to verify the effectiveness of backup solutions.

Redundancy

Implement redundant systems to ensure that critical workloads continue to operate in the event of a failure.

Deploy failover mechanisms to automatically switch to backup systems in the event of a failure.
Define recovery time objectives (RTO) to ensure that critical workloads can be restored within acceptable timeframes.

11. Leverage Automation and Orchestration

Automation and orchestration tools significantly enhance the efficiency of managing a hybrid cloud. These tools streamline workflows, reduce manual effort, and improve performance.

Automation Tools

Use Infrastructure as Code tools to automate the provisioning and management of hybrid cloud infrastructure.

Implement configuration management tools to automate the configuration and deployment of applications.
Use scripting languages to automate repetitive tasks and streamline workflows.

Orchestration

Implement workflow orchestration tools to manage complex workflows across the hybrid cloud.

Use service orchestration tools to automate the deployment and management of services across multiple cloud providers.
Define policies to automate the orchestration of workloads based on predefined rules and conditions.

Automation is a powerful tool for streamlining operations and eliminating repetitive tasks. However, implementing automation in complex IT environments often requires orchestration.
Discover how orchestration and automation can transform your IT operations by reading our in-depth article.

12. Continuous Improvement

Regularly reviewing and refining the hybrid cloud strategy maximizes its benefits and addresses emerging challenges.

Feedback Loop

Establish mechanisms to gather feedback from users on the performance and usability of the hybrid cloud environment.

Collect feedback from stakeholders to understand their needs and expectations.
Analyze feedback to identify areas for improvement and prioritize enhancements.

Iterative Improvement

Collect and analyze performance data to identify trends, patterns, and areas for improvement.

Use iterative development methodologies to continuously refine the hybrid cloud strategy based on feedback and performance data.
Encourage exploring new technologies and methods to enhance the hybrid cloud.

What Are the Benefits of Hybrid Cloud Adoption?

Here are some the key advantages of adopting a hybrid cloud strategy:

Cost Optimization

Hybrid cloud reduces capital expenditure by leveraging public cloud services. Instead of investing heavily in on-premises hardware, organizations can leverage the scalable resources public cloud providers provide. This approach allows businesses to avoid the significant upfront costs of purchasing and maintaining physical infrastructure.

Also, the hybrid cloud offers pay-as-you-go pricing models that align costs with actual usage. This model allows organizations to only pay for the resources they consume, which can lead to substantial cost savings. During periods of low demand, businesses can scale down their cloud usage and reduce costs. Conversely, during peak periods, they can scale up to meet increased demand without incurring the high costs of maintaining excess capacity.

For example, an ecommerce platform can automatically scale its web servers during a holiday sale to handle increased traffic and then scale back down once the sale ends.

Scalability

One of the most compelling advantages of a hybrid cloud is its elasticity. Organizations can handle peak loads by leveraging public cloud services without needing extensive on-premises infrastructure. This scalability is crucial for businesses that experience fluctuating demand.

A hybrid cloud also offers the flexibility to adapt to changing business needs by quickly provisioning or de-provisioning resources. This agility allows organizations to respond swiftly to market conditions, new opportunities, or unexpected challenges.

For instance, a startup can quickly scale its IT infrastructure to support rapid growth without significant upfront investments in hardware.

Flexibility

A hybrid cloud allows optimal workload placement, enabling organizations to choose the best environment for each workload based on specific requirements. For example, mission-critical applications that require high availability and low latency can be hosted in a private cloud. In contrast, less critical applications can be hosted in the public cloud to take advantage of cost savings.

Moreover, the hybrid cloud enables the customization of private cloud environments to meet specific business requirements. Organizations can tailor their private cloud infrastructure to support unique workloads, compliance needs, or security protocols.

For instance, a healthcare provider can design its private cloud to meet stringent data privacy regulations while benefiting from the scalability and cost advantages of the public cloud for non-sensitive workloads.

Legacy System Integration

Hybrid clouds can accommodate legacy systems that may not be suitable for migration to the public cloud. Many organizations have critical applications and systems that have been used for years and are deeply integrated into their operations. These legacy systems often require specific hardware or software configurations that are not easily replicated in a public cloud environment. Organizations can use a hybrid cloud to keep these legacy systems on-premises while benefiting from the public cloud's scalability and flexibility for other workloads.

For example, a financial institution can maintain its legacy mainframe systems on-premises while using the public cloud for newer, more flexible applications.

Hybrid Cloud: The Best of Both Worlds

Organizations can achieve a more agile, cost-effective, and secure IT infrastructure that supports their business goals and operational needs by adopting a hybrid cloud strategy. The hybrid cloud model enables businesses to leverage the strengths of different computing environments to meet their specific requirements for security, compliance, performance, and cost.

Server Management: Tools, Benefits, and Best Practices

Servers are essential to most IT functions, including data storage, web hosting, emails, and app functionalities. Due to their pivotal role, servers require continual management and maintenance to ensure longevity, efficiency, and adequate security.

This article is a decision-maker’s guide to server management. Read on to learn about the main aspects of server management and see how SMBs and enterprises keep their data storage healthy and efficient.

Server Management Definition

Server management is the process of administering a server to ensure optimal and safe performance. The main objective of this IT activity is to keep the server and its associated systems in a desired, consistent state.

Managing a server requires various administrative and maintenance tasks. The staff needs to:

Minimize server slowdowns and downtime.
Maintain server hardware and address technical problems.
Keep software up to date.
Design and implement system structures.
Monitor server apps by tracking their status, uptime, and recurring issues.
Run fault management tests.
Perform troubleshooting.
Implement robust backup strategies and cybersecurity solutions.
Monitor server traffic for suspicious activity.
Ensure servers keep up with requirements as business needs evolve.

Depending on the size of the IT setup, server management can be the task of a single admin or an entire team. While an admin can operate on an in-house level, companies often opt to outsource server management. Different providers offer different services, so choose a vendor that meets your requirements.

Want to take the operational burden off your team? Our managed services offer the most well-rounded and flexible server management on the market.

Both servers within data centers and in the cloud require some form of management. The most common types of servers a team can manage are:

FTP (File Transfer Protocol) servers that handle to-and-from data transfers between a server and devices.
Web servers that host websites.
Virtual servers that run on VMs.
Proxy servers that initiate and manage communication between a client and an external server.
Database servers that store large amounts of data.
Application servers that store and run web apps and plug-ins.

Our comparison of web and application servers outlines the differences and similarities between the two common server types.

Server Management Tasks

The goal of server management is to improve efficiency and performance while ensuring the safety of IT operations. Below are the main tasks the server management team needs to perform.

Setup and Configuration

Setting up the server and configuring software, add-ons, and functionalities is a core aspect of server management.

The setup process varies for different server types. An admin must know how to set up a server with physical components and one running on a VM in a third-party cloud.

The configuration also varies across different server types and use cases. For example, a server that hosts a blog needs a different platform than one providing e-commerce services. Configuring a typical Linux server requires an admin to go through the following steps:

Set up user configuration (credentials, privileges, access levels, etc.).
Set up network configuration and communications.
Work on package management.
Update the installation and patch potential vulnerabilities.
Set up a firewall and iptables to minimize external footprint.
Prevent clock drift via NTP configuration.
Set up SSH to protect remote sessions.
Work on daemon configuration to reduce the attack surface.
Protect the kernel and apps with SELinux.
Set up a logging system.

Business needs dictate server configuration. An admin must review all business, hosting, and server requirements to determine the correct settings and specifications.

Precise capacity planning is vital to server management. When setting up hardware, an admin must carefully consider the required specifications. Excess storage and processing ensure good performance but can also lead to unnecessary upfront costs and energy usage.

Hardware Management

Keeping hardware in good health is a vital aspect of in-house server management. Without reliable hardware, all systems and operations that rely on the server can run into issues. A server admin must monitor three primary hardware components:

Central processing unit (CPU). Overusing the CPU can lead to various problems. A CPU running close to 100% utilization overtaxes the device, leaves no capacity for extra tasks, and slows down the server. Admins typically upgrade the chip, add more CPUs, or stop unnecessary programs to handle an overused CPU.
Random-access memory (RAM). RAM is a server’s working memory that operates faster than a hard drive. The more RAM a server has, the better its potential performance. Admins must monitor RAM usage and determine when the system requires additional memory.
Hard drive. The hard drive acts as a server’s permanent storage for programs, data, and backups. Performance can take a hit when a hard drive works at maximum capacity, so admins must add drives or delete unnecessary data when disks fill up.

Monitoring the server’s temperature also falls under hardware management. Admins typically rely on wired thermometers and cooling fans to prevent devices from overheating.

If you host your servers in the cloud, you don't need to worry about hardware maintenance. The only exception is if you host a server on a VM running on an in-house private cloud. In that case, you need an admin to keep the dedicated hardware in good shape.

Software Management

Just like hardware, server software requires regular monitoring and maintenance. An admin must:

Understand software dependencies within the infrastructure.
Know how to search for software vulnerabilities that could lead to a potential data breach.
Keep software, firmware, and the operating system up to date with the latest patches.
Uninstall old and inactive programs the team no longer uses.
Ensure developers constantly update the code to remove bugs and weaknesses.

Most companies use Linux servers as this open-source platform is the most economical and secure OS for servers. Companies that rely on Windows servers typically have apps that only work on that operating system. Whatever the OS, the admin needs to keep the system up to date with the latest patches to prevent cyber attacks.

Unsure what OS to use on your server? Our head-to-head comparison of Linux and Windows servers outlines the factors you need to consider to make an informed decision.

Server Monitoring

Constant monitoring helps an admin keep servers safe and working at peak performance. Metric tracking and analysis allow the team to identify and prevent issues before they affect business-critical systems.

Monitoring hardware is vital. An admin needs live data evaluation that provides real-time feedback in terms of:

Processor usage.
Memory utilization.
Disk space availability.
Server room temperature.

Hardware monitoring aside, an admin should monitor processes running on the server and track how much resources each process consumes. The team must also keep track of the following parameters to guarantee top performance:

Page load time.
Database lags.
Service up-time.

Robust server management also requires reviewing access logs, unusual traffic spikes, and unauthorized login attempts. Odd logins and traffic behavior are clear signs of possible intrusion attempts.

Alerts are a mandatory aspect of robust server monitoring. An admin typically sets benchmarks for heavy traffic, poor disk usage, or overheating. If the server breaches a specific threshold, a notification via SMS or email alerts the staff.

Our article about server monitoring tools analyzes the best options on the market and helps you pick the right tool for your IT team.

Server Security

Maintaining a secure network also falls under server management. While security policies and requirements differ between industries, an average admin needs to:

Install, optimize, and regularly update your antivirus solution.
Set up a firewall to filter unauthorized traffic.
Create a credentials policy that ensures users have strong passwords.
Run regular vulnerability assessments.
Set up access control software and enforce zero-trust security.
Initiate and participate in penetration testing.
Use encryption to protect sensitive data.
Choose and implement SIEM tools.
Promote cybersecurity best practices across different teams and departments.

Our article about server security teaches simple and effective ways to boost your server’s safety.

Backup and Recovery

Regular data backups are essential to the security of servers and the information they host. Backups can either run on an in-house physical infrastructure or in the cloud. In both scenarios, an admin should use an immutable backup to ensure data remains safe even if intruders breach the server.

Besides using an immutable backup, other good practices when setting up server backups is to:

Back up data multiple times a day.
Use network segmentation to separate the backup from other systems.
Provide limited access to the backup.
Regularly scan backups for malicious data.
Limit editing permissions.
Optimize RTO (Recovery Time Objective) and RPO (Recovery Point Objective) to ensure a smooth user experience during backup.

The server’s power supply also needs a backup. A reserve power supply ensures you do not lose data or experience downtime in case of a power outage.

Who Needs Server Management?

Every business that owns or relies on a server requires server management. From one-person operations to large enterprises with stacked data centers, server management is not optional. The only question is if you can self-manage your equipment or hire a third party to do the job.

If you have the personnel with the proper skill set, managing servers in-house offers total control over the environment. When the team lacks experience, outsourcing server management makes more sense than investing in training and new employees.

If you choose the in-house option, you need a server management tool to automate processes and provide insights into the equipment. Not all solutions offer the same features, however, so consider the following factors when looking for the right tool:

Ensure the solution is compatible with all server types and apps in your stack.
Look for a program with an intuitive UI and visualization capabilities that simplify advanced tasks.
The software should allow the team to set thresholds and custom alerts.
Consider how much training the staff will require before they start using the tool.
The tool should provide an abundance of detailed, thorough data about the state of the server.
Look for a solution that offers rich automation features that streamline workflow and help employees save time.

Some companies choose to rely on a mix of in-house management and outsourcing. A popular option is to have the in-house staff handle software and hardware management while an outsourced expert works on server security and backups.

Learn about server automation, how to achieve it and how your business can benefit from it.

Advantages of Server Management Services

Choosing to hire a service provider to drive your server management has many benefits. Here are the most notable reasons why outsourcing a server admin is a good investment:

Top-tier experts. Providers specialize in server management, so third-party admins have more experience than an average in-house team.
Lower operational costs. Most server management services are subscription-based. An average monthly or yearly fee is far less expensive than investing in an in-house team of full-time personnel.
Reliable support. Most service providers offer 24/7 support to their customers. On-call support with guaranteed response times is ideal for preventing downtime.
Eliminate time-consuming tasks. Managing a server requires consistent monitoring, optimization, and analysis. By assigning these time-consuming duties to a vendor, your staff can focus on more impactful tasks.
Faster turnaround. If you side with the right partner, the turnaround time for server setups and updates is always quick.

Not sure what type of server is the right fit for your use case? Our comparison of bare metal cloud and dedicated servers weighs the two popular options.

Server Management is Not Optional

Effective server management prevents downtime, security breaches, and performance issues. Failing to set up a proper strategy can lead to devastating consequences, so either train your team to perform server maintenance or hire experts to ensure your operations stay smooth and efficient.

Data Center Power Monitoring

Power-related issues were the direct cause of 52% of all data center outages in the last three years. Since around 54% of these incidents resulted in damages that exceeded $100,000 (16% led to losses of over $1 million), it's clear why facility owners see data center power monitoring as a top priority.

Real-time power monitoring allows operators to identify and address power-related problems before they cause disruptions. This precaution prevents unexpected downtime, prolongs equipment lifespans, and ensures continuous service availability.

This article provides an in-depth guide to data center power monitoring. Jump in to learn about the ins and outs of tracking power consumption at data centers and see how seasoned facility managers avoid costly power disruptions.

Check out our article on data center power designs for a detailed look at how organizations ensure their facilities have reliable access to stable power sources.

What Is Data Center Power Monitoring?

Data center power monitoring is the practice of continuously tracking and analyzing power consumption and performance within a data center. The main goal of this type of monitoring is to ensure efficient, reliable, and continuous power delivery across the facility while minimizing downtime and optimizing energy usage.

Data center managers can track power-related metrics of individual IT devices, specific power components, individual racks and cabinets, server rooms, or entire facilities.

Here's an overview of what metrics operators most commonly track:

Power usage effectiveness (PUE). PUE measures energy efficiency by comparing the total power used by the facility to the power used by IT equipment alone. Lower PUE values indicate greater efficiency.
Energy usage (kWh). This metric tracks the total amount of electricity consumed by the data center or specific components of it.
Voltage (V). This metric monitors the electrical potential provided to data center equipment. Maintaining the correct voltage is crucial to ensuring stable operations.
Current (Amps). This metric measures the flow of electrical current to various components. Monitoring amperage ensures equipment is receiving the appropriate amount of power.
Carbon usage effectiveness (CUE). This metric tracks the amount of carbon dioxide emitted per kilowatt-hour of energy.
Power factor. This metric represents the ratio of real power used to perform work (kW) to apparent power (kVA) supplied to the circuit. A high power factor indicates efficient electricity use, while a low power factor suggests inefficiencies.
Load balancing. This metric tracks the distribution of power across circuits and equipment.
Energy efficiency ratio (EER). EER measures the ratio of cooling output to power input. A higher EER indicates better cooling efficiency.
Circuit capacity. This metric monitors the total capacity of each power circuit and how much of that capacity is being used.

Of all the components within a data center, cooling equipment actually uses the most power (often more than 50% of the facility's total consumption). Our article on data center cooling explains why keeping hardware at optimal temperatures is one of the most expensive and challenging aspects of running a data center.

Most common causes of power-related problems at data centers

Why Is Data Center Power Monitoring Important?

Data center power monitoring is crucial to maintaining efficient and stable operations. Here's an overview of why this type of monitoring is so vital to facilities of all tiers:

Less chance of downtime. Monitoring power usage allows operators to detect potential issues (power surges, overloads, equipment malfunctions) before they cause a full-blown outage. Real-time power monitoring directly translates into better uptime and less risk of violating terms of service level agreements (SLAs).
Lower operational costs. Tracking energy consumption enables data center operators to pinpoint areas that waste energy and make adjustments to improve power usage. This process leads to more effective power allocation, prevents overconsumption, and reduces energy bills.
Prolonged equipment lifespan. Monitoring power ensures that all equipment receives proper voltage and current. As a result, operators prevent power fluctuations from damaging the equipment and shortening its lifespan.
Compliance with industry regulations. Many facilities must adhere to strict regulatory standards regarding energy usage and environmental impact (ISO 50001, Energy Star, local government mandates, etc.). Power monitoring helps collect the necessary data to demonstrate compliance with these regulations and avoid hefty fines.
Capacity planning. Power monitoring helps data center operators understand their current energy needs and better predict future requirements. These insights enable teams to accurately assess whether the facility has enough power to support future growth without causing system overloads.
Smaller carbon footprints. Effective power monitoring helps data centers improve sustainability by providing insights into how facilities can optimize energy consumption and lower carbon emissions.

Power monitoring also plays an indirect role in data center security. Sudden power outages and voltage fluctuations often cause equipment shutdowns, which can create opportunities for threat actors to exploit system vulnerabilities.

Power Components Monitored in a Data Center

The table below provides an overview of the most notable power components that require round-the-clock monitoring:

Component	Role Within the Data Center	Benefits of Monitoring This Component
Main Electrical Feed	Brings external power from the utility grid into the data center.	Detects voltage fluctuations that could lead to equipment damage or service interruptions.
Backup Generators	Provide emergency power during grid outages.	Ensure reliable operation during grid failures.
Uninterruptible Power Supply (UPS)	Provides emergency power until the backup generator becomes operational.	Guarantees seamless transition from the grid to the backup generator.
Automatic Transfer Switches (ATS)	Switch power load to backup sources during grid failure.	Ensure uninterrupted transitions between power sources.
Power Distribution Units (PDUs)	Distribute electricity from the primary power source to equipment.	Prevent overloads and ensure balanced power distribution.
Remote Power Panels (RPPs)	Distribute power from PDUs to specific racks and equipment.	Maintain balanced distribution and avoid localized overloads.
Transformers	Step down high voltage from the utility grid.	Prevent overheating and failures due to excessive loads.
Circuit Breakers	Protect electrical circuits from overloads.	Prevent electrical damage by detecting overloads and faults.
Busbars and Switchgear	Distribute electricity across circuits and manage switching between power sources.	Ensure proper load distribution and detect system inefficiencies.
Fuse Panels	Protect circuits from excessive current by disconnecting in case of overload.	Maintain circuit safety and prevent damage from excessive current.
Cooling System Power Components	Power the systems that regulate facility temperatures.	Ensure consistent and cost-effective power delivery to cooling equipment.
Voltage Regulators	Maintain a constant voltage level to stop fluctuations.	Prevent equipment damage caused by voltage instability.
Capacitors	Store electrical energy and smooth out fluctuations.	Ensure stable power flow and prevent fluctuations from impacting performance.
Power Busways	Provide modular power distribution to equipment.	Improve load management and identify faults before they cause failures.

Prefer not having to worry about all these components? Consider offloading that responsibility by renting servers from or collocating hosting equipment at a third-party data center.

How Is Power Monitored in a Data Center?

Data center operators monitor power by using a combination of hardware and software to measure the electrical consumption and distribution throughout the facility. To do that, staff members use various power monitoring devices that continuously gather real-time data on:

The total consumption of electrical power across the data center.
The voltage supplied to the equipment.
Power quality (e.g., frequency, phase balance, harmonic distortion).
Electrical current flows.
The ratio of real power used to the apparent power drawn from the source.

Devices log and store monitored data for future analysis, which helps teams track trends, identify recurring issues, and plan for capacity upgrades. Most operators also use monitoring devices to set predefined thresholds for:

High power consumption.
Power quality issues (e.g., voltage sags, surges, frequency deviations).
Irregularities that could lead to equipment damage or operational failures.
Temperature-related issues.

Many data center operators use monitoring software to automate responses if a system exceeds a certain threshold. Common automated responses to power-related problems include:

Switching to backup power during an outage or critical power failure.
Distributing power demand to prevent overloads.
Automatically disconnecting problematic circuits to contain the impact of power issues.
Shutting down non-critical systems to reduce the load on the power supply.
Regulating voltage levels to ensure stable voltage across all equipment.

Many data centers use Data Center Infrastructure Management (DCIM) software to centralize and streamline power monitoring. DCIM systems integrate data from all monitoring devices to provide a centralized view of power usage across the facility. DCIM tools also generate detailed reports on energy usage and efficiency, which helps make informed decisions when optimizing power consumption.

Best practices for data center power monitoring

Data Center Power Monitoring Solutions

Data center power monitoring solutions are specialized tools designed to provide comprehensive visibility and control over power consumption. These solutions help facility managers optimize energy efficiency and prevent power-related failures.

There are four different types of power monitoring solutions:

Real-time monitoring solutions. These tools provide immediate feedback on power consumption and alert operators whenever they identify anomalies.
Predictive analytics solutions. These tools use artificial intelligence (AI) and regression algorithms to anticipate power requirements and likely system failures.
Historical data analysis systems. These solutions track energy usage trends over time to help with long-term planning and capacity management.
Automated control systems. These solutions automatically take action when systems exceed predefined power-related thresholds.

Most data centers rely on third-party solutions, as creating a custom tool takes a lot of skill, resources, and time. Here's an overview of some of the market's top data center power monitoring solutions:

Schneider Electric EcoStruxure Power Monitoring Expert. EcoStruxure is a comprehensive power management platform that enables real-time monitoring, analytics, and control of power systems in a data center.
Eaton Intelligent Power Manager (IPM). Eaton's IPM software offers real-time power monitoring, load balancing, and various energy-saving modes for small-to-medium facilities (i.e., those with fewer than 25 UPSes and PDUs).
Vertiv Environet Alert. Environet Alert provides real-time visibility into power and environmental data across the data center.
Panduit SmartZone G5 Intelligent PDUs. Panduit's G5 PDUs offer intelligent power monitoring with real-time data on power usage, environmental factors, and equipment performance.
Raritan Power IQ for Data Centers. Raritan's Power IQ aggregates data from PDUs and UPSes to provide real-time monitoring of power, energy, and environmental metrics.
Siemens SENTRON Powermanager. The Powermanager provides detailed insights into power consumption and quality across the data center.

Want to improve your IT monitoring? Check out our articles on the best server and cloud monitoring tools to see what solutions you can add to your toolchain.

Data Center Power Monitoring Cost

The cost of monitoring power at a data center depends on several factors. Here's what primarily determines the cost:

The size of the data center. Larger facilities require more monitoring equipment to cover the increased number of servers, racks, and networking equipment.
The complexity of the monitoring system. The more features a monitoring system has, the higher the cost. Tools with advanced features (predictive analytics, alerting capabilities, integration with other systems, etc.) cost more to acquire and set up.
Specific industry requirements. Some data centers, especially those in highly regulated industries like finance or healthcare, require more advanced monitoring capabilities to comply with industry regulations.

Implementing a power monitoring system involves significant upfront costs. The initial cost ranges from a few thousand to several hundred thousand dollars, depending on the scale and sophistication of the monitoring system. Here are the main factors that determine the upfront cost of setting up a power monitoring system:

Equipment costs. You must purchase the necessary devices, meters, and sensors. High-quality equipment can range from a few hundred to several thousand dollars each.
Installation costs. Installation costs vary based on the complexity of the setup and technicians' hourly rates.
Staff training. Teams often require training on how to use power monitoring systems effectively and safely. These training sessions cost time and money to organize.
Software costs. Many power monitoring solutions require specialized software for data analysis and reporting. Licensing fees vary widely, from a few hundred to thousands of dollars annually, depending on features and deployment scale.

Data center power monitoring also involves considerable ongoing costs. Facilities require regular maintenance of monitoring equipment to ensure accuracy and reliability. This process commonly involves routine calibration (typically done quarterly), software updates to add new features or security patches, and technical support.

An Essential Aspect of Data Center Management

Regardless of whether you run just a few on-site servers or are in charge of a full-blown hyperscale data center, your IT staff must keep a close watch over power usage. Proactive power monitoring will help you avoid costly disruptions, keep critical infrastructure in good health, and meet your uptime goals.

What Is Hybrid Cloud? Definition, Benefits, and Use Cases

When it comes to cloud environments, businesses today search for efficiency, agility, and flexibility of resources. The hybrid cloud is an excellent option for businesses with evolving IT infrastructures that focus on efficiency, scalability, and data security.

This article explains everything you need to know about the hybrid cloud and its infrastructure, how it works, and its benefits.

What Is Hybrid Cloud?

A hybrid cloud combines the elements of public and private cloud environments into an aggregated solution. It allows organizations to host sensitive data and applications in the private cloud while simultaneously utilizing the resources of the public cloud. This helps them optimize the IT infrastructure through the scalability, cost-effectiveness, and agility this combination offers.

Learn about the phoenixNAP hybrid computing solutions and choose the right one for your organization.

How Does Hybrid Cloud Work?

A hybrid cloud allows data and applications to move seamlessly between the public and private environment based on the changing business needs and workloads. The public cloud is suitable in cases of sudden spikes in demand for resources or when testing during development phases. While doing so, sensitive data can reside in the private cloud for additional security.

Hybrid Cloud Components

Two components of a hybrid cloud are public and private.

The private component. Located within the organization’s own data center or a dedicated hosting solution. It provides a controlled environment for storing sensitive data, applications, and workloads. The private component also manages compliance and ensures data sovereignty over the organization’s cloud assets. A single organization is the only user of the dedicated resources, which guarantees security and performance stability without noisy neighbors.
The public component. Offered by external cloud service providers, such as Azure, AWS, or Google Cloud. The CSPs provide on-demand resources and easy scalability for organizations to suit their fluctuating workloads. In addition, public clouds allow access to their data centers across the globe, so the geographic location does not affect latency and access times.

Hybrid Cloud Infrastructure

The hybrid cloud infrastructure combines physical and virtual components of private and public clouds by using dedicated lines, virtual private networks (VPNs), or software-defined networking (SDN) technologies.

In the private cloud, organizations either invest in personal data centers or outsource data center services (such as colocation or data center as a service) to set up their infrastructure. They decide how many resources they need for their data and applications and may choose to ensure on-premises maintenance and management or opt for managed private cloud services provided by third-party vendors.

On the other hand, in the public cloud, the infrastructure is managed solely by the cloud service provider. The data centers exist in all parts of the world, allowing remote access to resources to all users, no matter their location. Organizations utilize these resources based on the pay-as-you-go method, which saves money and allows easy scalability when needed.

Learn about the differences between the multi-cloud and the public cloud to choose your best option.

Hybrid Cloud Benefits

Hybrid cloud offers many advantages for your business. The section below provides a list of the most prominent hybrid cloud benefits.

Scalability. Hybrid cloud resources are easily scaled on demand to fit changing workloads.
Cost-effectiveness. A hybrid cloud allows users to optimize their cloud computing costs by allocating resources based on their consumption.
Business continuity. A hybrid cloud provides easy disaster recovery in case of data losses or other disruptions to business operations.
Innovation and agility. The hybrid cloud offers the most advanced solutions to organizations to keep them competitive in the market.
Global reach. Hybrid clouds allow access to resources and data centers from all over the world.
Vendor diversity. A hybrid cloud prevents vendor lock-in by allowing organizations to freely choose the most suitable cloud hosting option.
Easy integration. Hybrid clouds allow organizations to smoothly transition between old systems and modern cloud solutions.
Resource optimization. A hybrid cloud ensures the best resource optimization so that no service remains underutilized.
Availability. Hybrid clouds are designed with redundancy and failover mechanisms that prevent disruptions and guarantee high availability.
Data security and compliance. A hybrid cloud ensures the highest data security and compliance with regulatory standards.

For more information, refer to our guide to hybrid cloud security.

Challenges of Hybrid Cloud Implementation

However, implementing a hybrid cloud also brings some challenges:

Complexity. Implementation of a hybrid cloud requires a high level of expertise to avoid potential disruptions along the line.
Data mobility. Organizations must ensure a seamless transition of data between the two clouds, which is a challenging task that includes adjustments and system integrations.
Performance uncertainty. While the private cloud guarantees uninterrupted performance, the public cloud comes with a set of challenges that the hybrid cloud should remediate.
Governance and control. Private and public clouds have different permissions and levels of transparency for their data centers.
Skills gap. Between the public and the private cloud, there is a different set of skills for implementation and management. The staff must be equipped with the knowledge of both to successfully handle the hybrid cloud implementation.
Resource underutilization: Hybrid cloud services could be underutilized if the organization does not properly plan resource utilization to save costs.
Cost management. Predicting expenses between the public and the private cloud can be challenging and lead to unexpected costs.

Note: Businesses planning on choosing phoenixNAP solutions can predict their hybrid cloud costs using our cloud pricing calculator.
Regardless of your cloud provider, simplify cloud cost management efforts and optimize your spending with these 14 hand-picked cloud cost management tools.

Who Should Use a Hybrid Cloud Solution?

A hybrid cloud is a suitable solution for businesses that wish to utilize the best of both worlds in cloud hosting. Organizations with limited budgets benefit from hybrid clouds due to their cost-effectiveness.

Global companies that require remote access to data centers all around the world find hybrid cloud a smart solution for their business, especially if their industry includes strict regulatory and compliance demands. A hybrid cloud is also suitable for fast disaster recovery and an uninterrupted business operation.

Striking the Perfect Balance

A hybrid cloud is a dynamic solution for organizations that wish to combine the scalability, agility, and flexibility of public and private clouds. The hybrid cloud architecture has huge potential to bring out the best in organizations from all industries, so keep this in mind when choosing your cloud service provider and cloud hosting environment.

Strong Password Ideas For Greater Protection

Weak and easy-to-guess passwords make even the soundest cybersecurity strategy easy to bypass. If a hacker guesses or cracks a password, the intruder can access your account or system without raising the alarm and compromise assets you kept safe behind a password.

The guide below provides 11 strong password ideas that will help you stay a step ahead of hackers. We also explain the difference between sound and weak passphrases, provide tips on improving current passwords, and show the main methods hackers rely on to crack credentials.

How to Create a Strong Password (with Examples)

A strong password is a unique word or phrase that a hacker cannot easily guess or crack. Here are the main traits of a reliable, secure password:

At least 12 characters long (the longer, the better).
Has a combination of upper and lowercase letters, numbers, punctuation, and special symbols.
Random and unique.

While complexity improves password security, length is the key characteristic. The best way to make a password strong is to make it long. For example, look at these two passwords:

89&^598
ILoveMyCatLordStewart

While 89&^598 is entirely random, the first password is less secure than the second one. A password-cracking program could guess the 89&^598 in about 44 hours, while cracking ILoveMyCatLordStewart would require 7 years of constant processing.

However, even the 7-year mark is not enough to call a password safe, which is why all strong password ideas below lead to phrases that take significantly longer to crack.

Note: Use phoenixNAP password generator tool to securely generate strong and complex passwords .

The 4 Random Words Method

One of the simplest yet most effective strong password ideas is to combine 4 or more seemingly random words together. Just make sure that:

The password is at least 12 characters long.
The words do not have any natural flow to them (such as My Name Is Steven).
You separate words with either spaces, punctuation, or special symbols.

Some examples of these passwords (and how to remember them) include:

Phoenix Drive Cafe Office ("I work in Phoenix and drive by a cafe every day on my way to the office").
Seattle, Kindle, Coffee, Planes ("Seattle is the birthplace of Amazon, Starbucks, and Boeing").
Minnesota Airplane Boston Christmas ("I live in Minnesota but fly back home to Boston every Christmas").

The time needed to crack the Phoenix Drive Cafe Office password: 2 million years

Use an Entire Phrase

If you do not want to remember a random sequence of words, you can make a password out of a custom phrase. Words within a phrase flow together better than random words and are easier to remember, but you should not rely on a famous saying or a quote.

You can decide whether to include spaces between the words (if the website accepts spaces within passwords). Here are a few good examples of custom phrases:

You can actually use spaces in your password!
Myboysareinthehighschoolbasketballteam
I would prefer to go to Gryffindor pls

The time needed to crack the You can actually use spaces in your password! password: 4 hundred trillion years

Use a Custom Acronym

You can use an acronym to create a memorable yet effective password. For example, you can choose the phrase "My son was born at a Liverpool hospital in 2002" and take the first letter of each word (Mswb@aLhi2002) to create a solid and easy-to-remember password.

If you choose this method, ensure you are not basing the password on a common expression (such as Tb,on2b,titq). Here are some good ideas:

IoaBMW,wa5782p. ("I own a BMW, with a 5782 plate.").
H!Mnpintd2r! ("Hey! My new password is not that difficult to remember!").
2015wtyIbm1h. ("2015 was the year I bought my first house.").

The time needed to crack the IoaBMW,wa5782p. password: 42 million years

Use the Keyboard Layout

Using the keyboard layout to create a custom pattern is another strong password idea. For example, you can remember something simple as a name (e.g., Jane Austen) and then use the keys above and to the right of the letters (Iwj4 W8e64j). Some good examples are:

P05r 0t 6u4 %9jye ("Lord of the Rings").
Y5wjr F4j65wp ^45k9jwp ("Grand Central Terminal").
J43 &05o F4j65wp _w5o ("New York Central Park").

The time needed to crack the P05r 0t 6u4 %9jye password: 698 million trillion years

Make a Simple Formula

You can make up a custom formula to create a reliable password. For example, you can take any phrase and replace every letter with the next one in the alphabet:

Cucumbers are tasty! -> Dvdvncfst bsf ubtuz! (time needed to crack: 762 billion trillion years)

You can also take the first letter of every word from the chorus of your favorite song:

Mamma Mia chorus -> MmhIgammhcIrymmdisammjhmimy (time needed to crack: 129 million trillion years)

These examples may seem like gibberish, but that is exactly what you want to achieve.

Vowel Switching

Take any phrase and replace one vowel with another (for example, A with E). As always, have at least 12 characters and use a random phrase for max protection:

"Every Monday, I wish it was Friday 🙁 " -> Every Mondey, I wish it wes Fridey : (
"I like a pub with an all-night open-bar policy" -> I like e pub with en ell-night open-ber policy
"I hammer nails, but nails hammer the board" -> I hemmer neils, but neils hemmer the boerd

The time needed to crack the Every Mondey, I wish it wes Fridey : ( password: 307 million trillion years

Shorten Each Word

Pick a memorable phrase and remove the first three letters of every word (do not worry if the process deletes the entire word):

"Workdays are for work, but weekends are for football!" -> kdays k, kends tball!
"Thursdays are great, but Fridays are better" -> rsdays at, days tter
"Basketball is my favorite sport after hockey" -> ketball orite rt er key

The time needed to crack the kdays k, kends tball! password: 184 billion years

The Sentence Method (Bruce Schneier Method)

Think of a random sentence and transform it into a password by taking the first two letters of every word. For example:

"I wish I had more time to think of better passwords…" -> IwiIhamotitothofbepa…
"Driving by a McDonald's and not stopping takes a lot of willpower" -> DrbyaMcDoannosttaaloofwi
"Creating a strong password is not that hard after all" -> Crastpaisnothhaafal

The time needed to crack the IwiIhamotitothofbepa... password: 1 billion years

Mix the ISO Codes of Favorite Countries

This fun yet strong password idea requires you to list the ISO codes of your favorite countries or countries you have visited (that way, you can update your password every time you visit a new nation). You will get something like this:

"Canada, Mexico, France, Germany, Japan" -> can mex fra deu jpn

The time needed to crack the "can mex fra deu jpn" password: 424 trillion years

The Math Method

You can use mathematical symbols and equations to create a strong password. These passwords are typically long and full of different symbols, making them an ideal passphrase choice. Some examples are:

MyDog+MyCat=8legs
830-630=TwoHundred
Children+Xmas=Presents

The time needed to crack the MyDog+MyCat=8legs password: 9 million years

Use a Deliberate Misspelling

You can intentionally misspell words to create unique and secure passwords, such as:

SuperrmenHatseCryptoss
KryingTeers2Nite
ILovDoubluMcBurgurs

The time needed to crack the SuperrmenHatseCryptos password: 119 million years

If you decide to use this method, be careful not to use common misspellings (such as "acommodate"). Hackers feed cracking programs with password lists with all the usual wording errors, so the more obscure your password is, the better.

Safe passwords are just the beginning of a sound security strategy. Learn what else you need to account for by referring to our article on the most common types of cyber attacks.

How to Improve an Existing Password

If you have a favorite password that you already find easy to remember, you do not have to substitute it with a new passphrase. Instead, you can improve the current weak password by:

Adding spaces or brackets.
Adding additional words.
Repeating the password twice.
Turning the passphrase into an email address.
Adding emoticons.
Adding random punctuation.
Swapping letters around (if the current password has enough characters to be secure).

Slight changes to a password are also helpful when creating unique passphrases for several accounts. Rather than creating a new password from scratch, you can add a different code to your existing password for each online account (e.g. {Andrew,77}EBAY for your eBay profile and {Andrew,77}PPAL for the PayPal account).

What to Avoid when Choosing a Password

You should follow a strict set of rules when choosing passwords to avoid weaknesses that a hacker can exploit. A strong password should never:

Have less than 12 characters.
Be solely based on personal data (name, surname, family member's name, date of birth, workplace, favorite sports club, etc.).
Contain memorable keyboard paths (most notably qwerty and asdfgh).
Solely use letters, symbols, or numbers.
Be reused across two (or more) different accounts or websites (even if the password is strong, one of the websites keeping the password might have a breach and place all other accounts in danger).
Include sequential letters or numbers.
Rely on basic character substitution for security (such as M@nch3st3rUtd or L3tM31n).
Be based on a common word (in any language).
Contain the corresponding username.

Examples of poor passphrases that may look like strong password ideas are:

5404464785: This password has no letters or special characters, plus it uses a phone number.
March101977: This password uses personal info (someone's birthday), has a common dictionary word (March), and lacks special characters.
P@ssword12345: While it does have 13 characters, a symbol, numbers, a mix of letters, and no personally identifiable info, a computer can crack this password in 0.01 seconds. P@ssword is a common phrase, and the 12345 sequence is a simple find for any program.

It is also wise to stay clear of any passwords that other people widely use. Hackers always start the cracking process by trying the most popular passphrases, such as:

111111
123123
12345678
jesus
letmein
password1
asdf
qwerty
trustno1
abc123
dragon
football
iloveyou

You can use the Have I Been Pwned? website to test how unique your password is and ensure your passphrase was not part of any prior data breach.

Additional Security Options to Secure Your Passwords

Besides strong password ideas, you can also rely on other security practices to ensure a password remains safe. The suggestions below are helpful both for securing personal credentials and protecting passwords on a company-wide level.

Multi-Factor Authentication (MFA)

Even if someone steals your password, you can still prevent the intruder from accessing your account. Multi-factor authentication (MFA) adds an extra layer of security to your account by requiring the user to provide the following during login:

A username and password.
A biometrics scan or a physical token.

This two or three-step verification process makes it difficult for cybercriminals to gain access and steal your identity.

If you wish to protect your business from stolen identities and passwords, you can implement MFA via a specialized app that your employees install on their smartphones. Google's Authenticator and Authy are two great free options, both of which generate a one-time PIN that serves as an additional factor during login.

Refer to our cybersecurity best practices article for more ideas and advice on protecting your business from cyber threats and check out our Biometrics vs. Passwords article to learn about the differences between these two security options.

Virtual Private Networks (VPNs)

You (and your employees) should always use a VPN when typing in or exchanging passwords on public Wi-Fi. A VPN ensures no one is intercepting your username and password when you log into your account.

Besides various other benefits, our Bare Metal Cloud offering also enables you to quickly and easily set up a remote access VPN.

General Password Protection Best Practices

Even the world's toughest password becomes pointless if you do not know how to use and protect it. Be careful with your passphrase by following these best practices:

Never share your password with anybody.
If you store passwords online, ensure the website does not store credentials in plaintext.
When choosing security questions in case of a forgotten password, select hard-to-guess options to which only you know the answer. Do not use a question whose answer is easy to find online or on your social media.
Do not save your passwords in an online document, email, or note.
Change passwords regularly, at least once every few months.
Do not write down your password and store it near your workstation.
Do not keep the passphrase inside your phone (either as a note or picture).

You should also not allow browsers to save your password. While convenient, this feature means that a single data leak instantly compromises all your accounts.

Password Managers

A password manager keeps track of all your passwords and does the remembering for you. All you remember is the master password which grants access to the management program (which is, hopefully, a strong password protected with MFA).

Password managers keep passphrases safe with encryption. If someone successfully hacks the manager, password hashes would be useless without the decryption key, which is why sound key management is vital for these apps.

You can use a password management program to keep personal credentials safe or as a means of streamlining and securing the way your employees create, store, and use passwords.

Check out the best enterprise password management solutions on the market and see which one is the best fit for your workforce.

What Are the Common Techniques Used by Hackers to Crack Your Password?

Hackers use numerous techniques to crack passwords. Below is a list of the most common methods a cybercriminal can use to compromise your credentials.

Brute Force Attacks

A brute force attack is a simple process in which a program automatically cycles through different possible combinations until it guesses the target password. These programs can easily crack simple and medium passwords.

An average brute force program can try over 15 million key attempts per second, so 9 minutes is enough to crack most seven-character passphrases. Brute force attacks are the main reason why we insist on a 12-character minimum for passwords.

Learn how to prevent brute force attacks with 8 effective yet easy-to-implement tactics and precautions.

Dictionary Attacks

Whereas a brute force attack tries every possible combination of symbols, numbers, and letters, a dictionary attack tries to crack the password via a prearranged list of words. This attack typically starts with common categories of words, such as:

Sports teams.
Names of celebrities, family members, friends, pets, TV/film characters, etc.
Places (countries, cities, landmarks, etc.).
Hobbies.
Animal names.

A dictionary attack also tries substituting letters with symbols, such as 1 for an I or @ for an A. This cyberattack is the main reason why no security-aware person should use common words in their password.

Phishing Attacks

A phishing attack happens when a criminal tries to trick or pressure you into unwittingly sharing credentials. This social engineering threat often relies on emails: hackers send an email pretending to be someone else and refer users to fake login pages.

For example, you (or one of your employees) can receive an email detailing a problem with your credit card account. The email directs to a link that leads to a login page on a phony website resembling your credit card company. If the victim falls for the trick, the hacker who created the false website receives the credentials on a silver platter.

Eavesdropping

A hacker can intercept credentials when victims exchange passwords via unsecured network communications (without VPN and in-transit encryption). Also known as sniffing or snooping, eavesdropping allows a hacker to steal a password without the victim noticing something is wrong.

Keylogging Viruses

A keylogging virus watches every keyboard press you make, enabling a hacker to record your passwords (among other activities).

Dridex and Zeus are the two most common keylogging viruses. Both malicious programs spread through infected email attachments and primarily look for banking login details. To avoid these viruses, you should:

Know how to identify phishing emails.
Keep your computer software up to date.
Install and use a robust anti-virus tool.
Avoid questionable websites and software.

Think Dridex and Zeus are bad? Wait until you read about the most dangerous ransomware examples and their impact on businesses around the globe.

Credential Recycling

Credential recycling is a less targeted attack, but still dangerous to people without a strong password. This tactic uses usernames and passwords collected in other breaches and tries them on as many random platforms and websites as possible.

Hackers typically gather tens of thousands of different credentials leaked from another hack. Unfortunately, as many people use the same simple passwords, this method is very effective. Another name for credential recycling is password spraying.

Do Not Take Any Chances with Your Passwords

If someone steals or guesses your password, that person can easily bypass all other security measures protecting your data. The strong password ideas in this article can help keep you safe and ensure your passphrases never end up in the wrong hands.

5 Test Automation Frameworks: Overview and Use Cases

Test automation frameworks provide valuable resources (libraries, guidelines, coding standards, etc.) that teams rely on to perform and manage automated software testing. Frameworks help developers and testers create highly effective testing strategies while enabling companies to boost the ROI of their QA departments.

This article takes you through five different test automation frameworks that make automated testing faster and more reliable. We provide high-level overviews of every framework, explain its main pros and cons, and offer advice on picking a framework that best fits your team's priorities and IT needs.

Already familiar with the basics of automation frameworks and want to read about specific platforms? Check out our article on the market's top automation testing tools.

What Are Test Automation Frameworks?

A test automation framework is a set of guidelines and resources that help design, implement, and execute automated test scripts. A framework makes it easier to create tests by providing teams with the following:

High-level testing strategies and guides.
Coding standards (typically based on Java, C#, Python, or JavaScript).
Code libraries for everyday testing tasks (interacting with the tested software, verifying expected outcomes, reporting test results, etc.).
Various testing tools, programs, and compilers.
Mechanisms to manage test data (e.g., input values, expected results, test configurations, etc.), execute scripts, and generate reports.

Here's an overview of the essential components you'll find in most platforms that enable you to implement a test automation framework:

The AUT (Application-Under-Test).
Test data.
Driver scripts.
Environment variables.
Business user library.
General user library.
Recovery scenarios.
An object repository.
Test execution reports.

Test automation frameworks provide various benefits that help develop and execute test scripts. Here are the most notable advantages of these frameworks:

Improved test speed and accuracy.
Reduced cost of testing.
A modular and scalable structure for script development.
More productive teams.
Lower risk of human errors during testing.
More test code reusability.
Fewer bugs in production.
Reduced chance of test case duplication.

The popularity of test automation frameworks grew alongside the rise of the DevOps culture. A common DevOps principle is to run tests as early and as often as possible within the CI/CD pipeline. This task becomes significantly easier with a framework that helps organize, execute, and keep tests consistent.

Recent reports reveal that the global test automation market will reach $28.8 billion by 2025 (up from $12.6 billion in 2020, which is a CAGR of 18.0%). The most significant factor driving this growth is the increased adoption of DevOps and agile testing practices.

Types of Test Automation Frameworks

Below is a list of the five test automation frameworks companies use to make testing faster and more reliable. Read on to see which one makes the most sense for your testing requirements.

Linear Automation Test Framework

Linear automation is the most straightforward of all test automation frameworks. The team must:

Create a series of automated test scripts for each functionality that requires testing.
Run test scripts individually when it's time to test a feature.

Testers write a single program in sequential steps without any modularity. For example, if you want to verify that the creation of a new Gmail account works correctly, you'd write a script with the following steps:

Open gmail.com.
Click on "Create Account."
Enter details.
Verify details.
Create an account.

Linear frameworks offer simplicity and clear test coverage for individual functionalities, making them a go-to strategy for testing simple features.

Main pros of the linear automation framework:

Simple to understand since each test script focuses on a specific functionality.
Scripts are simple and easy to write.
Requires little upfront planning.
Makes it easy to isolate and debug problems.
Clear test coverage.
Highly granular reporting and result tracking.

Main cons of the linear automation framework:

Teams must create test scripts for each functionality separately.
The linear nature limits the ability to handle complex test scenarios with conditional branching.
Testers must embed all the data directly into the test code, so there's no way to use the code for any other data set.
Error-prone and challenging to maintain.

Modular-Based Testing Framework

The modular-based testing framework breaks up the tested software into multiple isolated components (i.e., modules) with separate test scripts. Each module focuses on a specific functionality or feature.

Once testers break down the application into modules, they write a test script for each component. Then, testers combine individual scripts to build larger test cases hierarchically.

All testers who rely on modular-based frameworks build an abstraction layer to ensure changes in individual sections do not affect the overarching module. Another go-to strategy is to use design patterns such as Page Object Model (POM) or Model-View-Controller (MVC).

Main pros of the modular-based testing framework:

High level of modularization.
Modules are reusable, which promotes efficiency in testing and reduces duplication.
Enables easy expansion of the test suite (either add new modules or combine existing ones).
A change to one module is reflected in all related test cases, which makes it easy to maintain and update tests.
Modules compartmentalize test logic, which makes test code more manageable and structured.

Main cons of the modular-based testing framework:

Creating reusable modules is time-consuming.
Requires careful upfront planning for more complex apps.
Interdependencies between components often cause problems.
Test scripts must have hard-coded data.
Most tests require a specific order of module execution, which often affects execution speed.

How often do companies run automated tests

Data-Driven Test Framework

The data-driven test framework separates test data from script logic:

Test scripts contain the necessary steps and actions (scripts are parameterized, so they accept input values dynamically).
Test data contains the input values and expected results for each test case.

That way, testers get to test the same feature or function multiple times with different data sets. The team instructs scripts to read and populate the necessary data when needed.

You store test data in an external database (Excel spreadsheet, text file, CSV file, SQL tables, ODBC repository, etc.) with key-value pairs, enabling testers to create reusable test scripts or cases for testing different data sets. There is also no need to hard-code data into the script itself (unlike the linear or modular-based testing framework).

Main pros of the data-driven test framework:

Allows testers to reuse test scripts with different sets of data.
Makes test data easy to update and modify independently of test scripts.
Significantly reduces the number of scripts required to cover all the test scenarios.
A broad test coverage that allows teams to test various scenarios and data combinations.
Easily accommodates a growing number of test cases (just add more data sets as the test suite expands).

Main cons of the data-driven test framework:

Requires setting up and managing an external data source, plus infrastructure for accessing and managing test data.
Testers must ensure a proper sync between test scripts and data sets.
Requires advanced knowledge of a test script programming language.
You must ensure data integrity, accuracy, and completeness of test data to avoid false positives and false negatives.
Identifying the root cause of failures in scripts is often challenging.

Keyword-Driven Test Framework

The keyword-driven test framework (also known as the table-driven or action-driven framework) separates human-readable instructions (i.e., keywords) from the underlying test automation logic. A tester creates a set of keywords that represent necessary test actions or operations, such as:

Interacting with UI elements.
Entering data.
Verifying results.
Clicking buttons.
Navigating through different screens.

The framework maps keywords to corresponding functions that carry out the actual actions on the app. You store keywords in a step-by-step fashion with an associated object, like in this example:

Step number	Description	Keyword	Object
Step 1	Click on the login button on the Home Page	clicklink	Login button
Step 2	Enter username	inputdata	Login username field
Step 3	Enter password	inputdata	Login password field
Step 4	Verify user login info	verifylogin	Submit button

With this framework, testers create scripts by simply specifying the sequence of keywords and associated test data.

Main pros of the keyword-driven test framework:

Human-readable keywords make test scripts easy to understand.
Easy modification and adaptation of test scripts to different scenarios.
Reduced duplication of code and improved maintainability of test suites.
Testers get to reuse keywords across multiple test cases.
Excellent test coverage and scalability due to the ability to combine different keywords.

Main cons of the keyword-driven test framework:

Requires time and effort to define keywords.
Testers must learn the framework's unique conventions and structures.
Failing to define keywords clearly leads to confusion, errors, and testing limitations.
The team must manage and regularly update the centralized repository of keywords, associated actions, and test data.

Hybrid Test Automation Framework

The hybrid approach combines elements of multiple test automation frameworks to create a custom strategy that suits your specific testing requirements. Here are a few common examples:

Using keywords to define high-level test instructions and also relying on the data-driven framework to handle various test data sets.
Organizing test cases into modules while also sequentially creating and executing test scripts for each functionality.
Using the data-driven framework to handle various test data sets and the linear automation framework's sequential execution of scripts.

The exact implementation of the hybrid model varies depending on the project and the organization's needs.

Main pros of the hybrid test automation framework:

You get to select different elements from various frameworks to address specific testing needs and achieve optimal test coverage.
Highly flexible and provides the ability to handle complex and diverse testing requirements.
Reduces duplication of effort, improves efficiency, and enhances maintainability (when done right).

Main cons of the hybrid test automation framework:

Requires knowledge of different frameworks and expertise in integrating them effectively.
Takes more time and effort to set up than when you use a single, off-the-shelf framework.
Changes or updates to any of the integrated frameworks impact the overall framework, so expect frequent tweaks to your testing strategy.
Troubleshooting issues is more complex than when using a single framework.

Concerned about how a test automation framework will impact your security? Consider implementing SecOps (a security-first approach to software development) to boost the overall safety of your entire pipeline.

Which Automation Testing Framework Should You Use?

Start by thoroughly evaluating your project and testing requirements. Consider the following factors:

The app under test (type, involved technologies, architecture, supported platforms, features, overall complexity, etc.).
Required test coverage.
Long-term goals and upcoming plans for the app.
Budget and licensing restrictions.
Testing tools and practices already in place.

Once you thoroughly understand your project, determine what framework features you require. A few common capabilities and traits companies typically look for are:

Test code reusability.
Scalability.
The ease of test script maintenance.
Compatibility with existing CI/CD tools and processes.
Community support and documentation.
No cascade of failures (i.e., one test failing does not stop the entire testing process).
Support for plugins or extensions.

Next, assess the team's expertise and preference by gathering input from all relevant stakeholders (developers, testers, and project managers). Determine the staff's familiarity with different frameworks, programming languages, and automation tools. Choosing a framework that your team has prior experience in ensures a smoother adoption process and fewer issues during testing.

Here are a few general pointers to keep in mind when choosing a framework:

Simpler projects and less experienced teams do well with linear and modular-based testing.
Apps with intricate workflows and interactions are typically an excellent fit with data-driven testing.
Consider the keyword-based framework if your testing process involves a few non-technical stakeholders.
Think twice before investing time and money into creating a hybrid framework.
If your apps undergo frequent updates, choose a framework that provides robust mechanisms for maintaining test scripts.

Always conduct one or more proof of concept (POC) projects before making the final decision. Try several frameworks on a few small test scenarios to assess how they align with your needs. Only go "all-in" on a framework if it performs well during these pilot tests.

Make Full Use of Test Automation

Choosing the right test automation framework limits the number of bugs in production, increases overall test accuracy, and turns apps into more reliable products. Additionally, automated tests boost the cost-effectiveness of QA processes, so select a framework that fits your SDLC and start making full use of test automation.

DevOps Pipeline: What It Is and How to Build One

The era when developers had years to create and launch new software products is a thing of the past. Today, users expect their favorite applications to feature the latest updates and enhancements at unprecedented speeds.

Software development companies must implement effective DevOps pipelines to meet these high expectations. They are essential for staying ahead of customer demands and maintaining a competitive edge.

This article explains the fundamental concepts of a DevOps pipeline and how it operates in a DevOps environment. It also outlines the various stages that code undergoes before it is deployed to production.

What Is DevOps Pipeline?

A DevOps pipeline is a structured set of practices that development (Dev) and operations (Ops) teams use to build, test, and deploy software more efficiently and reliably. Its purpose is to streamline the software development process, ensuring it remains organized, focused, and capable of delivering high-quality products rapidly.

Types of DevOps Pipeline

DevOps pipelines come in various forms, each tailored to specific needs. Here are the primary types:

Continuous Integration (CI) Pipeline

Continuous integration is a practice where developers frequently merge their code changes into a central repository. Each merge triggers an automated build and testing process to ensure the new code integrates smoothly with the existing codebase. The key benefits of CI pipelines include:

Early detection of integration issues.
Reduced time spent on debugging and fixing conflicts.
Improved code quality through automated testing.

Learn more about software testing methodologies.

Continuous Delivery (CD) Pipeline

Continuous delivery extends the CI pipeline by automating the deployment process to staging environments. In a CD pipeline, code changes that pass the automated tests are automatically prepared for a release to production. This approach allows organizations to:

Release updates more frequently and reliably.
Reduce the risk of deployment errors.
Maintain a consistent and repeatable deployment process.

Continuous Deployment Pipeline

Continuous deployment takes continuous delivery further by automating the entire release process. Any code change that passes all pipeline stages, including production-like testing, is automatically deployed to production without manual intervention. This type of pipeline offers:

Faster delivery of new features and bug fixes to end-users.
Immediate feedback on the impact of code changes in the production environment.
Greater agility and responsiveness to market demands.

Learn more about the differences between continuous integration, delivery, and deployment to see how each practice optimizes stages in the development pipeline.

Microservices Pipeline

Microservices architectures require specialized pipelines to handle the deployment of individual services independently. A microservices pipeline ensures that each microservice can be built, tested, and deployed separately, allowing for:

Increased flexibility and scalability in the development process.
Independent updates and rollbacks for each microservice.
Improved fault isolation and recovery.

Infrastructure as Code (IaC) Pipeline

Infrastructure as Code pipelines automate the provisioning and management of infrastructure through code. This type of pipeline allows teams to define and manage infrastructure using version-controlled configuration files. Key advantages include:

Consistent and repeatable infrastructure deployments.
Simplified infrastructure management and scaling.
Enhanced collaboration and visibility through code review processes.

Security Integration Pipeline

Also known as DevSecOps, this pipeline type integrates security practices into the DevOps workflow. Security integration pipelines automate security checks and compliance validations at every stage of the development process. Benefits include:

Early detection and mitigation of security vulnerabilities.
Improved compliance with security standards and regulations.
Enhanced overall security posture of applications.

DevOps Pipeline Components

Here are the core components of a DevOps pipeline:

Source code repository. A central hub for storing and managing code, enabling version control, collaboration, and change tracking.
Continuous integration (CI) server. Integrates code changes from multiple developers into a shared repository, automating builds and running tests to detect integration issues early.
Build automation tools. Compile source code into executable artifacts, ensuring consistent and reliable builds.
Automated testing frameworks. Execute various tests (unit, integration, performance, security) to ensure code functionality, performance, and security.
Configuration management tools. Automate deployment and management of infrastructure and application configurations, ensuring consistent and reproducible environments.
Containerization and orchestration tools. Package applications into containers and manage their deployment, scaling, and operation.
Artifact repository. Stores built artifacts (binaries, libraries, Docker images) for deployment to different environments.
Continuous deployment (CD) tools. Automate deploying code changes to production, ensuring quick and reliable releases of new features and updates.
Monitoring and logging tools. Provide real-time insights into application performance and behavior in production, aiding in quick detection and diagnosis of issues.

DevOps Pipeline Stages

Here are the stages of a typical DevOps pipeline:

1. Source Code Management (SCM)

Source code management involves using a centralized version control system to manage the source code. This stage allows multiple developers to work on the codebase simultaneously, ensuring that all changes are tracked and conflicts are minimized. Developers use branching strategies like feature branches to work on individual tasks. Code changes are merged back into the main branch through pull requests or merge requests after review and approval. The repository maintains a history of all changes, enabling rollback if needed.

If you are using Git, you may find the following guides useful:

2. Continuous Integration (CI)

Continuous integration (CI) is the practice of frequently integrating code changes from multiple developers into a shared repository. Each integration triggers an automated build process, compiling the latest code and executing unit tests. This process ensures that code changes integrate smoothly and helps detect issues early in the development cycle.

3. Automated Testing

Automated testing includes executing a suite of tests to validate the code's functionality, performance, and security. These tests can be categorized into:

Unit tests that verify individual components.
Integration tests that check the interaction between components
Acceptance tests that ensure the software meets business requirements.
System tests that validate the complete system.

Check out our article on the best automation testing tools.

4. Artifact Management

Artifact management involves storing build artifacts, such as binaries, libraries, and Docker images, in a repository. This ensures that artifacts are versioned and available for deployment to different environments. Artifact repositories provide a single source of truth for built components, making retrieving and deploying the correct version easy.

5. Continuous Delivery (CD)

Continuous delivery automates code deployment to a staging environment after it passes all tests. This stage ensures the code is always deployable and can be released anytime. The CD process includes:

Packaging the application.
Deploying it to a staging environment.
Running additional tests to verify the deployment.

6. Staging Deployment

Staging deployment involves deploying the application to a staging environment that closely mimics the production environment. This stage allows for final testing and validation before the release. The staging environment is used for end-to-end testing, user acceptance testing (UAT), and performance testing. This stage ensures that the application performs as expected under real-world conditions.

7. Performance and Security Testing

Performance and security testing are critical to ensuring the application can handle expected loads and is secure from vulnerabilities. These tests evaluate the application's responsiveness and stability under load, and scan for security risks. This stage ensures the application is robust and secure before deployment to production.

8. Continuous Deployment

Continuous deployment automates the process of deploying code changes to the production environment. The application is automatically deployed to production after passing all tests in the staging environment. This stage ensures that new features and updates reach users quickly and reliably. Continuous deployment minimizes manual intervention, reducing the time and effort required for releases.

9. Monitoring and Feedback

Monitoring and feedback involve continuously observing the application's performance and collecting user feedback. This stage uses monitoring tools to track key performance metrics, logs, and error rates. Real-time insights are provided into the application's health and performance, enabling rapid response to issues. User feedback is also collected to inform future development.

How to Build a DevOps Pipeline?

Building an effective DevOps pipeline starts with a clear vision of your objectives and requirements. Identify your goals, such as speeding up deployments, improving code quality, and reducing manual errors. These objectives will shape the tools and processes you adopt, guiding your team toward a seamless and efficient workflow.

Begin by establishing a robust version control system. This system will be the backbone of your development process, allowing multiple developers to collaborate without conflicts. A well-defined branching strategy and consistent commit guidelines will help maintain an organized and traceable codebase, which is essential for smooth integration and continuous development.

Automation is the heart of a successful DevOps pipeline. By automating the build process, you ensure that code is consistently compiled and tested, reducing the risk of errors. Integrate automated testing to verify your code's functionality, performance, and security at every stage. This continuous feedback loop is crucial for maintaining quality standards. Centralizing build artifacts in a repository also ensures they are versioned and ready for deployment, streamlining the transition from development to production.

Implementing continuous delivery practices will automate code deployment to staging environments, allowing for thorough testing and validation before reaching production. This step reduces the time and effort required to release new features and updates, ensuring they are reliable and ready for end-users.

Finally, robust monitoring and feedback mechanisms should monitor application performance and gather user insights. This real-time data is invaluable for rapid issue resolution and ongoing improvements, ensuring your pipeline evolves with your needs and continues to deliver value.

Read our article on DevOps roles and responsibilities to learn how different roles and responsibilities influence the pipeline.

DevOps Pipeline Tools

Here is a comprehensive list of essential DevOps pipeline tools categorized by their functionality:

Source Code Management Tools

SCM tools are used for version control, collaboration, and tracking changes in the source code.

Git. A distributed version control system for tracking changes in source code during software development.
GitHub. A web-based platform that uses Git for version control and provides collaboration and project management features.
GitLab. An integrated DevOps platform that combines Git repository management, CI/CD, and more.
Bitbucket. A Git repository management solution designed for professional teams.

Continuous Integration Tools

CI tools automate the process of frequently integrating and testing code changes from multiple contributors.

Jenkins. An open-source automation server used to automate building, testing, and code deployment.
Travis CI. A CI service that integrates with GitHub to automate testing and deployment.
CircleCI. A CI/CD platform that automates the software development process.
TeamCity. A CI and build management system by JetBrains.

Build Automation Tools

Build automation tools compile and build the code into executable artifacts.

Maven. A build automation tool used primarily for Java projects.
Gradle. A build automation tool that supports multi-language development.
Ant. A Java-based build tool from Apache.

Automated Testing Tools

Automated testing tools ensure that code meets quality standards and functions as expected.

JUnit. A unit testing framework for Java.
TestNG. A testing framework inspired by JUnit but designed to be more flexible.
Selenium. A tool for automating web browsers, used for UI testing.
JMeter. An open-source tool designed for performance testing.
OWASP ZAP. A tool for finding security vulnerabilities in web applications.

Artifact Management Tools

Artifact management tools store and manage build artifacts, such as binaries and libraries.

JFrog Artifactory. A universal artifact repository manager that supports all major package formats.
Nexus Repository. A repository manager that manages binaries and build artifacts.
AWS CodeArtifact. A fully managed artifact repository service by AWS.

Configuration Management Tools

Configuration management tools automate the provisioning and configuration of infrastructure.

Ansible. An open-source automation tool for configuration management, application deployment, and task automation.
Puppet. An automation tool for managing infrastructure as code.
Chef. A configuration management tool that automates infrastructure provisioning.
Terraform. An open-source tool for building, changing, and versioning infrastructure safely and efficiently.

Containerization and Orchestration Tools

Containerization tools package applications and their dependencies into containers, while orchestration tools manage these containers' deployment, scaling, and operation.

Docker. A platform for developing, shipping, and running applications in containers.
Kubernetes. An open-source system for automating containerized applications' deployment, scaling, and management.
OpenShift. A Kubernetes-based platform for container orchestration.

Continuous Deployment (CD) Tools

CD tools automate the deployment process, releasing code changes quickly and reliably.

Spinnaker. An open-source, multi-cloud continuous delivery platform.
Argo CD. A declarative, GitOps continuous delivery tool for Kubernetes.
Octopus Deploy. A deployment automation tool that enables teams to manage releases and deployments.

Monitoring and Logging Tools

Monitoring and logging tools provide real-time insights into application performance and behavior.

Prometheus. An open-source systems monitoring and alerting toolkit.
Grafana. An open-source platform for monitoring and observability, often used with Prometheus.
ELK Stack. A suite of tools (Elasticsearch, Logstash, Kibana) used for searching, analyzing, and visualizing log data.
Splunk. A platform for searching, monitoring, and analyzing machine-generated data.

For more information, read our in-depth comparison of the best CI/CD tools.

What Are the Benefits of Building a DevOps Pipeline?

A DevOps pipeline significantly improves your software development and deployment process. Here are the key benefits:

Increased Deployment Speed and Frequency

A well-structured DevOps pipeline automates many aspects of the software development lifecycle, allowing faster and more frequent deployments. This agility enables you to:

Release updates quickly, delivering new features, bug fixes, and improvements to users.
Respond swiftly to market demands, adapting to changing market conditions and customer feedback.

Improved Collaboration and Communication

DevOps pipelines promote collaboration between development and operations by breaking down silos and promoting a culture of shared responsibility. This results in:

Enhanced teamwork, where developers and operations work together towards common goals.
Reduced miscommunications, as there is a clearer understanding of roles and responsibilities across teams.

Higher Quality Code

Automated testing and continuous integration ensure that code is consistently tested and validated throughout development. This leads to:

Identifying bugs and integration problems early in the development cycle.
Consistently maintaining high standards of code quality and reliability.

Reduced Risk and Improved Stability

By automating the deployment process and incorporating continuous testing, DevOps pipelines reduce the risk of errors and improve the stability of software releases. This results in:

Fewer deployment failures, as manual errors and deployment issues are minimized.
Stable production environments ensure that new code integrates seamlessly with existing systems.

Enhanced Security

DevOps pipelines can integrate security practices into every stage of the development process, leading to:

Proactive vulnerability management.
Compliance with standards, such as GDPR, HIPAA, and PCI DSS.

Continuous Improvement and Feedback

DevOps pipelines facilitate continuous monitoring and feedback, allowing teams to learn from each deployment and improve their processes. This continuous improvement cycle leads to the following:

Iterative enhancements through incremental changes to the software and processes.
Real-time insights into application performance and user behavior.

Greater Efficiency and Productivity

Automation of repetitive tasks reduces the time and effort required from development and operations teams, leading to:

Increased efficiency thanks to a streamlined development process and a reduction in manual work.
Higher productivity allows teams to focus on strategic and creative tasks.

Better Resource Management

DevOps pipelines optimize the use of resources, ensuring that infrastructure is used efficiently and cost-effectively. This includes:

Easily scaling resources up or down based on demand.
Reducing unnecessary infrastructure costs through efficient resource utilization.

Our article on the nine crucial DevOps principles will help your organization get the most out of DevOps.

DevOps Pipeline Cost

Here's a breakdown of the various cost components associated with building and maintaining a DevOps pipeline:

Tooling costs. The selection of tools for your DevOps pipeline significantly impacts costs. Some tools are free, while others have pricing models based on subscriptions or usage.
Personnel costs. Human resources are a significant part of the overall cost. Consider the salaries for DevOps engineers who set up, manage, and maintain the DevOps pipeline, the costs for training existing staff on new tools and processes, and ongoing support and maintenance expenses.
Infrastructure costs. Running a DevOps pipeline requires IT infrastructure. Infrastructure costs include servers (on-premises or cloud-based), storage for code repositories, build artifacts, logs, backups, and networking costs, especially for distributed teams or cloud environments.
Integration and customization costs. Integrating various tools and customizing the pipeline to fit specific workflows requires engineers to spend time developing and potentially hiring external consultants or vendors to assist with setup and integration.
Operational costs. Ongoing operational expenses include monitoring and logging, backup and disaster recovery, and incident management.
Security and compliance costs. Ensuring the pipeline is secure and compliant adds costs. Expenses include scanning, vulnerability assessment, and compliance checks and audits.

Whether you're starting from scratch or trying to refine your existing processes, our 9-step DevOps implementation plan will equip you with the knowledge and tools to successfully apply DevOps.

Maximizing Efficiency: The DevOps Pipeline Advantage

A DevOps pipeline is a strategic investment that can transform how you develop, test, and deploy software. It streamlines collaboration, accelerates release cycles, and ensures code quality, greatly boosting efficiency and productivity.

While implementing a DevOps pipeline requires significant upfront investments in tools, infrastructure, personnel, and security, the returns are substantial. Faster time-to-market, optimized resource utilization, and continuous improvement are just some benefits.

By embracing DevOps, organizations gain a competitive edge, deliver exceptional software, and drive business growth.

Hybrid Cloud Security: Challenges and Best Practices

Hybrid clouds are highly advantageous but challenging to keep safe. Continuous data movement and tight integration between on-prem and cloud computing components create ample room for vulnerabilities. Organizations require a proactive security mindset and various precautions to protect their hybrid clouds reliably.

This article covers the essentials of hybrid cloud security and ensures you're ready to take on the task of protecting your IT environment. We discuss the most common challenges companies face when securing a hybrid cloud and present 10 tried-and-tested methods for improving hybrid cloud security.

Learn about the main >benefits and >challenges of hybrid clouds to evaluate whether this deployment model is the right choice for your business.

What Is Hybrid Cloud Security?

Hybrid cloud security is an umbrella term for various measures and practices designed to protect data, applications, and infrastructure within a hybrid cloud. The main goal is to ensure resource confidentiality, integrity, and availability across integrated on-prem and cloud environments.

Hybrid clouds pose unique security challenges because they combine concerns of on-site systems and cloud services. Organizations must secure each component of the hybrid model individually and ensure that the system is safe at the junctions between different IT environments.

High levels of security are essential to the success of the hybrid cloud deployment model. Here's what organizations get by boosting hybrid cloud security:

Reduced attack surface. An adequately secured hybrid cloud provides attackers with fewer ways to breach the system and set up a foothold.
Data integrity. High levels of hybrid cloud security ensure the integrity and confidentiality of sensitive data.
Operational resilience. Robust security measures help avoid system downtime. Organizations get to maintain business continuity even in the face of disruptive incidents.
Cost-efficiency. Effective security strategies lead to significant cost reductions. Organizations reduce the risk of costly data breaches, improve uptime, and optimize resource utilization.
More reliable compliance. Robust hybrid cloud security helps companies comply with strict data protection regulations like CCPA or GDPR.

Proactively planning for security is one of the essential initial steps when creating a well-rounded >hybrid cloud strategy.

What Are Hybrid Cloud Security Challenges?

Hybrid clouds pose unique security challenges due to the complexity and diverse nature of these IT environments. Below are the most common challenges of hybrid cloud security.

Data-Related Concerns

Adopters of hybrid clouds must decide which data sets belong within private data centers and which should reside in public or private clouds. Choosing the correct placement for each data set and deciding how to keep it safe is a major challenge of hybrid cloud security.

Continuous data transfers further complicate this issue. Data within a hybrid cloud must move between on-prem servers and cloud environments for processing, storage, backup, or disaster recovery purposes.

When data is in transit between on-prem and cloud systems, files are vulnerable to:

Interceptions and eavesdropping.
Man-in-the-Middle (MitM) attacks.
File modification or tampering.
Data replay attacks.
Packet sniffing and traffic analysis.
DNS spoofing and cache poisoning.

Security aside, adopters must also deal with the challenges of data synchronization, version control, and data validation. Additionally, organizations operating in regulated industries must adhere to specific data storage and movement compliance requirements.

Interoperability Issues

Interoperability challenges arise due to a hybrid cloud's need to integrate and operate seamlessly across diverse IT environments. Efficient and safe operations require smooth interoperability between on-site infrastructure and cloud-based services.

Hybrid cloud strategies often combine multiple types of public cloud services (IaaS, PaaS, and SaaS), frequently belonging to multiple providers. Each vendor has its own:

Security protocols.
Set of services.
APIs.
Networking configurations.
Management interfaces.

The lack of standardized protocols complicates security efforts. Organizations often need to develop custom solutions to facilitate communication and data exchange between on-premises servers and the cloud. Divergent models and controls introduce vulnerabilities if not correctly aligned.

As an extra issue, interoperability problems often impact the overall performance of hybrid cloud environments. Delays in data exchange, increased latency, and reduced efficiency are common problems.

Increased Attack Surface

Hybrid clouds have an amplified attack surface due to the combination of public clouds, on-prem infrastructure, and private clouds. Security admins must deal with numerous entryways through which a malicious actor can gain access or launch a cyber attack.

Each environment in a hybrid cloud has different settings and configurations. This diversity introduces multiple vectors attackers can target. Here are a few vulnerabilities commonly found in hybrid clouds:

Misconfigured security settings. Malicious actors often target inconsistent security configurations between on-prem and cloud environments.
Inadequate access controls. Weak or misconfigured access controls allow intruders to gain excessive privileges or access sensitive data.
Data interception. Data moving between on-prem and cloud components is a prime target for an attack.
Integration vulnerabilities. Hybrid clouds rely on APIs, gateways, and connectors for communication between different IT environments. Connections between components present opportunities for attackers to exploit flaws and gain unauthorized access or quietly set up a foothold.
Remote access vulnerabilities. Malicious actors often target users who remotely access hybrid cloud resources.
Third-party service exploits. Hybrid clouds frequently use third-party databases, analytics tools, and security services. These external dependencies are another common target for attacks.

The interconnected nature of hybrid cloud environments also amplifies the potential impact of a security incident (the so-called blast radius).

While closely related, attack surface and vectors are not synonymous terms. Learn the difference between these two vital security concepts in our >attack vector vs. surface article.

Diverse Security Models

Variations in security requirements in a hybrid cloud architecture present a major challenge. A hybrid cloud strategy must account for and unify the following security elements:

On-site security measures that keep IT equipment in server rooms safe.
Measures for protecting private cloud environments (whether hosted on-prem or at a third-party provider).
Security controls for protecting workloads and data residing in the public cloud.

Each environment in a hybrid cloud requires unique security controls, policies, mechanisms, and practices. Admins must secure components individually and integrate security models to create a safe hybrid environment.

For example, on-prem systems may rely on the traditional LDAP or a domain controller, while public clouds often use cloud-specific IAM solutions. Coordinating and ensuring consistency across these different mechanisms is vital for security.

Visibility Challenges

Monitoring in a hybrid cloud environment is often challenging due to the complex and dynamic nature of the infrastructure. Visibility challenges impact the ability to detect and respond to incidents promptly.

Several factors contribute to the difficulty of effective monitoring in a hybrid cloud, including:

The mix of multiple interluded yet separate IT environments.
Inclusions of varying service models, often from different vendors.
Shared data flows and processes that span multiple systems.
Complex networking configurations that involve VPNs, cloud on-ramps, and advanced routing.

All major cloud providers offer native monitoring, but integrating these tools with on-prem solutions is complex. Achieving a unified and holistic view of the entire infrastructure is a typical hybrid cloud security challenge.

Check out pNAP's >hybrid cloud solutions and see how we help companies deal with the challenges of hybrid cloud adoption.

Hybrid Cloud Security Best Practices

Securing a hybrid cloud involves a combination of different precautions, policies, and technologies. Below are 10 best practices for ensuring high levels of hybrid cloud security.

Invest in Network Security Controls

Network security controls are crucial for protecting data in a hybrid cloud. These precautions protect communication channels, prevent unauthorized access, and detect potential threats.

Here's what you can use to improve network security in a hybrid cloud setup:

Firewalls. Use firewalls to monitor and control incoming and outgoing network traffic. Set up firewalls at various points in the hybrid infrastructure, including between on-prem and cloud environments, within cloud networks, and at the network's edge.
Intrusion Detection and Prevention Systems (IDPS). IDPSes detect and respond to suspicious activities and potential security threats. These systems analyze network traffic for patterns indicative of malicious behavior and take automated action to mitigate threats.
Virtual Private Networks (VPNs). Use VPNs to establish secure connections over public networks. They are essential for securing communication between on-prem and public cloud environments.
Network ACLs. Use network Access Control Lists to control inbound and outbound traffic within cloud environments. Lists help enforce network security rules at the virtual network level.

As an extra precaution, consider segmenting your network to limit the lateral movement of threats. Segmentation helps contain and isolate incidents, reducing the impact of breaches and enhancing overall network security. Boosting endpoint protection is another impactful way to improve network safety.

Implement a Robust IAM Strategy

An Identity and Access Management (IAM) strategy is vital for hybrid cloud security. IAM involves managing and controlling access to resources, systems, and data based on user identities and assigned roles.

Hybrid cloud adopters require a unified approach to identity and access management across the hybrid environment. Most cloud service providers offer native IAM solutions that integrate with on-prem systems. This process typically involves unifying on-prem directories with cloud-based identity services.

Once you create a centralized identity management system, set up the following features and precautions:

Single Sign-On (SSO). SSO allows users to access multiple apps and services with a single set of credentials. SSO reduces the risk associated with employees managing multiple passwords.
Role-Based Access Controls (RBACs). Adopt RBAC to assign permissions to users based on their roles within the organization.
Multi-Factor Authentication (MFA). Require users to provide multiple forms of identification before gaining access to resources.
Privileged Access Management (PAM). Use PAM to control access to and the use of accounts with elevated permissions.

Use dynamic access policies that adapt based on contextual factors (e.g., user location, device type, or time of access). Dynamic policies add an extra layer of protection by tailoring access controls to specific conditions.

Consider integrating IAM logs with SIEM tools to correlate and analyze security events across the hybrid environment. SIEM tools detect patterns indicative of threats and are a must-have for most cloud security strategies.

Use Encryption Throughout the Data Lifecycle

Encrypting data throughout its lifecycle is a fundamental practice in hybrid cloud security. Encryption safeguards sensitive info from unauthorized access and lowers the threat of data leaks.

Use at-rest encryption to protect data hosted on on-prem servers. You should also encrypt your cloud storage with cloud-native encryption mechanisms provided by the cloud service provider (either through server-side encryption (SSE) or client-side encryption).

In-transit encryption is vital to keep traffic moving throughout your hybrid cloud safe. Use this type of encryption to protect the following data flows:

On-prem network traffic. Encrypt communication channels between systems within your local on-site infrastructure.
Inter-cloud communication. Encrypt all data that moves between different cloud environments.
Hybrid cloud communication. Secure communication between on-prem and cloud environments using encrypted channels.

Consider also using encryption in use to enable systems to process data without the need for decryption during computation. This precaution provides an extra layer of security for sensitive computations.

All three encryption strategies require careful key management to be effective. Follow key management best practices to ensure safe operations.

Our all-in-one >EMP enables you to centralize encryption efforts and control all keys from a single pane of glass.

Regularly Patch Systems

Establish a patch management policy that outlines procedures for identifying, testing, and applying patches in both on-prem and cloud environments. Your patch management policy must address challenges specific to patching in a hybrid cloud environment, such as:

Coordinating updates across multiple unified platforms.
Dealing with diverse operating systems and toolsets.
Managing dependencies between on-prem and cloud-based components.

Regular updates and patches address known vulnerabilities, minimizing the risk of exploits by cyber criminals. Careful patch management also reduces the risk of disparities between on-prem and cloud components, mitigating potential security gaps.

Consider using automated patch management tools that seamlessly integrate with both on-prem and cloud platforms. Automation streamlines the deployment of updates and reduces the time it takes to address vulnerabilities.

Back Up On-Prem and Cloud-Based Data

Data loss can occur for various reasons, including accidental deletion, hardware failures, software glitches, or malicious activity. Data backups provide a safety net, allowing an organization to recover lost or corrupted data in the event of issues with the original files.

Here's what you must do to effectively implement a data backup strategy in a hybrid cloud setting:

Regularly schedule automated backups of both on-prem and cloud-based critical data.
Store backups in geographically diverse locations.
Use immutable backups to prevent ransomware injections.
Encrypt backups to protect sensitive info.
Regularly test the restoration process to ensure backup viability.
Periodically review and update backup policies based on evolving business needs and security risks.

Data backups also reduce the risk of hybrid cloud service outages or disruptions caused by missing data. Backups provide an alternative source of critical info if something happens to the original data set.

Check out pNAP's >backup and restore solutions to see how we ensure our clients never experience permanent data losses.

Implement Zero Trust and PoLP

Zero Trust security and the principle of least privilege (PoLP) are vital to hybrid cloud environments. These strategies minimize the attack surface, limit the impact of incidents, and enhance overall security posture.

The Zero Trust security model is based on the "never trust, always verify" principle. A Zero Trust environment requires verification from everyone, whether inside or outside the network. Here are the key elements of zero trust security:

Users must provide credentials (2FA of MFA) whenever they log in to their accounts.
The system must continuously authenticate users and devices based on multiple factors (user behavior, device health, context, etc.).
Security admins must use granular micro-segmentation to isolate workloads and data.
Architects must apply security controls at the app layer.
Systems must analyze patterns and behaviors to detect signs of potential security threats or unauthorized activity.
The environment must assess compliance with security policies and the presence of up-to-date security software for all devices attempting to access resources.

PoLP is a natural fit for any Zero Trust strategy. PoLP grants users and systems the minimum level of access required to perform their specific tasks. This precaution limits the potential damage an intruder can do with a compromised account or device.

Educate and Train Employees

Provide employees with a contextual understanding of the hybrid cloud model, emphasizing the shared responsibility for security. Organize regular security awareness training that covers the following areas:

Specific security risks associated with a hybrid environment.
Guidelines on how employees navigate security risks when using the cloud.
Instructions on how team members can recognize and report indicators of security incidents, such as signs of phishing, traffic hijacking, or ransomware delivery.
Guidelines on how regulatory compliance requirements apply to data stored or processed in the hybrid cloud.

Tailor training programs based on employees' roles and responsibilities. Different teams have unique security considerations, so targeted training addresses specific needs. Also, consider running regular simulation exercises to check how prepared employees are to identify and thwart incidents.

Set Up Real-Time Monitoring

The goal of monitoring in hybrid cloud security is to provide an organization with real-time visibility into its security posture. Monitor user activities, network traffic, and system logs to detect security incidents in a timely manner.

Continuous monitoring enables organizations to detect:

Various anomalies indicative of potential risks.
Security breaches.
Unauthorized access attempts.
Signs of irregular cloud resource usage.
Opportunities to lower cloud computing costs.
Shadow IT usage (either on-prem or in the cloud).
Breaches of compliance measures.
Unwanted changes in configurations.
Missing patches and updates.

Monitoring activities must encompass the entire hybrid landscape, including data movement between on-site servers and the cloud. Set up robust logging mechanisms and real-time log analysis to promptly identify any irregularities.

Our article on >cloud monitoring tools presents 30 solutions that help ensure visibility across your cloud environments.

Establish Incident Response Plans

Incident response plans ensure a coordinated and swift reaction to security threats in times of crisis. Outline the most likely and impactful incidents that could occur within your hybrid cloud and prepare how teams should deal with these events.

Most hybrid cloud adopters prepare incident response plans for the following scenarios:

An intruder gaining unauthorized access.
Breaches of data moving between on-prem and cloud systems.
Data exfiltration attempts.
Malware infections.
Insider threats.
Disruptions in communication protocols.
Unforeseen local incidents (e.g., a power blackout or natural disaster).
Configuration errors that could lead to exposure.
A prolonged cloud outage.

Be as detailed as possible in your incident response plans. Set KPIs, calculate optimal RTOs and RPOs, outline clear priorities, and define go-to personnel.

Another good practice is to invest in disaster recovery, either by preparing a DR plan in-house or going with DRaaS. Sound DR ensures you experience little to no downtime once you deal with whatever went wrong within your hybrid cloud.

Our >disaster recovery checklist explains how to create an effective DR plan, plus provides a handy questionnaire to ensure you do not miss anything vital during planning.

Perform Regular Security Assessments

Run vulnerability assessments to identify potential weaknesses in configurations, software, and access controls across the hybrid cloud infrastructure. Perform these audits regularly, and also whenever the team makes any significant tech or infrastructure updates.

Insights gained from vulnerability assessments inform an organization about potential risks, areas for improvement, and adherence to security policies. If relevant to your business, also conduct compliance audits to verify that security measures align with industry regulations.

Another worthwhile way to check your hybrid cloud security is to run periodic penetration tests. These realistic simulations of real-world attacks assess the following areas:

Overall resilience and efficacy of security measures.
The teams' readiness to deal with threats.
The effectiveness of employee training programs and incident response procedures.

If running tests in-house is not an option, engage third-party security experts for independent testing. External assessments provide an unbiased, fresh-eyed perspective and bring seasoned specialists to the evaluation process.

Once you make a plan on how to adopt the best practices discussed above, outline all strategies in a >cloud security policy. A policy standardizes procedures, minimizes security gaps, and ensures a cohesive defense strategy.

Take Zero Chances with Hybrid Cloud Security

The hybrid cloud enables you to leverage the advantages of both on-site and cloud-based systems, but the model comes with a few must-know security challenges. Understanding data flow, access controls, and integration points can help mitigate these risks.

Use what you learned in this article to ensure your organization uses a hybrid cloud without any needless risks to security. Applying best practices also strengthens overall security posture and reduces the likelihood of breaches.