What is a Multi-Model Database?

Introduction

Multi-model database management systems unify multiple database systems into one. Instead of working with numerous models and finding ways to incorporate them together, multi-model databases provide a singular engine for various database types.

This article gives an in-depth overview of multi-model databases.

What Is a Multi-Model Database?

A multi-model database is a management system that combines multiple database types with a single backend. Most database management systems support only a single database model. On the other hand, multi-model databases store, query, and index data from different models.

Multi-model databases provide the modeling advantages of polyglot persistence without having to find ways to combine different models. The flexible approach allows storing data in different ways. The result is:

• Agile and flexible programming.
• Reduced data redundancy.

For example, exploring relationships between data points or building a recommendation system is much easier with graph databases. On the other hand, relational databases help define relationships between columns of data.

A vital multi-model database feature is the ability to transform data from one format to another. For example, data in JSON format quickly transforms into XML. Converting data formats provides additional agility and makes it easier to fulfill specific project requirements.

Multi-Model Database Use Case Examples

Use cases help provide an idea of how multi-model databases work. Analyzing practical examples provide better insight into how multiple models work together in one system.

Storing and Managing Multiple Data Sources

A typical IT system uses various data sources. The stored information is not always in the same format or database. Multiple formats create a complex system, making it hard to maintain and search through data.

Storing data in a multi-model database makes administration easier. Everything is in one database, which reduces the time needed to store and manage data from different sources.

Extending Model Features

Multi-model databases provide extensions between models. Features from some models help supplement the shortcomings of other models.

For example, querying data in JSON format using SQL queries is simple. There is no need to adjust the original data source. Extensibility reduces data processing time and eliminates the need for extract, transform and load (ETL) systems.

Hybrid Data Environments

A typical data environment keeps operational data separate from analytical. The data for analysis must be transformed and stored in a different place from the operational data.

The information duplicates, decreasing data quality. Likewise, the separated space creates maintenance overhead. Both databases need policy administration as well as backup management.

A multi-model database provides a hybrid approach to data storage. A unified data hub for storing transactional and extracting analytical data is simpler to maintain.

Data Centralization

Data within an organization has barriers. While restrictions must exist, this approach prevents utilizing information within a company.

Multi-model databases store data as-is without the need for transformations. Data centralization provides valuable insight into existing data, as well as the opportunity for creating new use-cases.

Searching Big Data

Hadoop is exceptional at processing large amounts of diverse data across different models. The main reason is the speed of receiving, processing, and storing various data. However, the one thing Hadoop lacks is an efficient search mechanism.

Taking advantage of the Hadoop processing power and combining it with the strength of multi-model database searches produces a robust system. The process of working with data becomes scalable and sturdy for big data tasks.

Multi-Model Database Advantages and Disadvantages

Multi-model databases come with benefits and drawbacks. The table provides the summary:

Pros	Cons
Data consistent	Complex
Agile	Developing
ACID compliant	Lacks modeling techniques
Suitable for complex projects	Not suitable for simple projects

The database model primarily works in corporate settings where a lot of data is present. Different sectors use the data for various tasks. However, an already established and specialized polyglot persistence structure will notice the lack of features in multi-model databases.

Advantages

The advantages of using multi-model databases are:

• Inter-model data consistency due to a single backend.
• Different data types on one platform provides an agile environment.
• Fault-tolerant due to ACID compliance.
• Suitable for complex projects which require multiple views of data.

Disadvantages

Some disadvantages of using multi-model databases are:

• Multi-model database systems are challenging to work with and complicated.
• The database model is still developing and has not matured properly.
• There is limited availability of different modeling techniques.
• Not suitable for simpler systems or projects.

What Are the Best Multi-Model Databases?

There are many different multi-model database types available on the market. The one distinguishable feature is the support for multiple models in one backed engine.

Some databases layer multiple models on the engine through components. However, these kinds of databases are not authentic multi-model databases.

Another critical difference between databases is the available modeling techniques. This aspect is essential for maximizing the usefulness of the available data.

MarkLogic Server

MarkLogic Server is a multi-model NoSQL database that started as an XLM storage and developed further to store multiple data formats, such as:

• Document
• Graph
• Text
• Spatial
• Key-value
• Relational

The database is versatile, efficient, and secure. The features of Mark Logic Server are:

• Security and governance. Integrated governance over data and user security.
• ACID compliant. Strong data consistency through ACID compliance.
• Advanced search. A built-in search engine with semantic search providing access to data.
• BI and analytics. Customizable analytics and business intelligence tools are readily available.
• Embedded machine learning. Intelligently automated data curation through embedded machine learning algorithms, providing quicker data access.
• Fault-tolerant and resilient. Mark Logic Server has high availability and disaster recovery systems to avoid any interruptions.
• Hybrid cloud support. The database allows self-managed deployment through hybrid cloud solutions.

ArangoDB

ArangoDB is a multi-model native database system. The supported data formats are:

• Document
• Graph
• Key-value

The database retrieves and modifies data through a unified query language, AQL. Some of the other notable features are:

• Advanced joins. Allows joining data with flexible querying, lowering the data redundancy.
• Transactions. Running queries on multiple documents with available isolation and transactional consistency.
• Sharding. Synchronous replication through sharding helps reduce internal cluster communication, improving performance and join speed.
• Replication. Replication provides a distributed database within one data center.
• Multi-threaded. The database takes advantage of multiple cores through multithreading.

OrientDB

OrientDB is an open-source multi-model NoSQL database written in Java. The database supports the following models:

• Document
• Graph
• Key-value
• Object
• Spatial

OrientDB was the first to feature multiple models at the core level. The database comes with many unique features, some of which are:

• SQL support. Queries in SQL are supported, making it easier for programmers to shift from relational models.
• ACID compliant. The database is fully transactional, providing reliability.
• Distributed. Full support for multi-master replication across different dedicated servers.
• Teleportable. Allows quick importing of relational databases.

Conclusion

Varieties of database modeling techniques exist, each providing a unique solution with benefits and drawbacks. Multi-model databases attempt to unify various databases into a single backend, reducing complexity and overhead as a system expands.