What is a multi-model database?
A multi-model database, as the name suggests, supports multiple data models on a single backend. The earliest database model was the SQL model. Other models not based on SQL, known as NoSQL databases, include key-value, document, and graph databases.
Multi-model vs. traditional database
Traditional SQL, or relational databases, are unable to meet the dynamic needs or the tsunami data influx of organizations today. Relational databases are more rigid and difficult to change once deployed, sometimes even requiring downtime. They organize data in the form of tables containing data in row structures.
One solution is to organize and copy the same data into different models, however, this data redundancy incurs storage costs as it has to be increased by manifolds which is not entirely sustainable or productive in many cases.
On top of that, ETL (extract, transfer, and load data) might also be required. ETL is a process in which data from different sources is collected and landed into a target destination that is not necessarily organized in the same way as their source. Transformation also requires certain actions such as data cleansing, formatting, standardization, aggregation, and normalization, etc., before it can be used for data analysis to yield actionable business intelligence.
The multi-model database acts as a single access and storage point for any application which incorporates features from different types of database models. This eradicates data redundancy and renders ETL obsolete, thus not only reducing computing and operational costs but also enhancing an application’s scalability.
The multi-model database has many advantages. For instance, it removes the need to install, configure and maintain different databases as a result of which the licensing fees are also reduced. It also opens possible future experimentations on the same backend with more models owing to the dynamic needs of businesses.
NoSQL database models
Multi-model databases can incorporate different database models. The NoSQL models include:
- Key-Value Store and Cache
- Document Database
- Graph Database
A key value database is a type of NoSQL database that stores data in the form of key-value pairs. Unlike tabular or graphic forms, every value has a corresponding unique key. To access a user’s data, the id can be set as the key which will return the entire data as the value. It consists of simple get, put and delete operations. This results in low latency and a highly scalable data storage solution which has many use cases such as user cache, history, and profile.
A document database is another type of NoSQL database which is a special type of key-value database. Instead of a single value, the key can contain a variable number of attributes, usually in the form of a JSON file.
This database model is an alternative to traditional schema databases as it can have different attributes as well as data types instead of fixed ones. Thus, this type is better suited to the constantly changing needs of modern businesses. Tech giants such as Samsung, Netflix, Google, and Amazon use document databases.
A graph database follows a parent-child relationship structure. A data query can be resolved by navigating through the child nodes of a parent until the desired data can be retrieved. A child is defined by their relationship to the parent nodes, hence making it an ideal alternative for modeling a database with complex relationships. The graph structure results in incredibly fast and efficient data access by traversing through nodes. In short, providing a model where relationships between entities can be established without the rigidness of a traditional database schema.
Businesses today need to keep up with incoming data at a high volume and pace. Moreover, the data needs to be processed to result in actionable business intelligence. Instead of using different database models and services, a multi-model database can be set up with a single back-end. This database model can incorporate features from different databases such as key-value, document, and graph database.
This strategy has many potential benefits including ease of use, low latency operations, and is less computationally and financially expensive. Finally, the configuration and maintenance of a single back-end are less cumbersome than managing different models from separate service providers.