Database Types

There are no special database types for AI. Instead, various database types and platforms are suitable for different learning models.


    : (Relational Database Management System) is the most extensively used and oldest type of database. It is easy to implement and administer. The top ten most popular relational databases are Oracle, MySQL, Microsoft SQL Server, PostgreSQL, IBM Db2, SQLite, Microsoft Access, MariaDB, Hive, and Microsoft Azure SQL Database.
  • NoSQL

    database type is used to store unstructured, semi-structured, and structured data, which might include serialized model files. It is scalable, responsive, and has minimal downtime. The ten most popular non-relational databases are MongoDB, Redis, Cassandra, Hbase, Neo4j, Oracle NoSQL, RavenDB, Riak, OrientDB, and CouchDB.
  • Operational

    database type manages and controls fundamental operations within an enterprise and may include AI directives. Collected data may be processed and viewed in real-time. These databases may be coupled with


    databases to provide a unified overview of information for planning, reporting, and decision-making.
  • Model Repositories

    may be deployed to manage and store machine learning models. Examples include MLflow, TensorFlow Serving, and Data Version Control. Docker containers, such as Docker Hub or AWS ECR, may also be used to package and deploy machine learning models.

Databases may be centralized, distributed, on-premises, or cloud-based.