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:

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.