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10 Best Databases for Machine Learning and AI [2024]

Last Updated : 14 Mar, 2024
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Machine learning and Artificial Intelligence are the two trending fields in the IT industries that involve a lot of technologies. Hence, databases are the main things which are used in Machine learning and Artificial Intelligence and they serve as a basic foundation for these trending technologies.

Databases for Machine Learning & AI

They also developed their predictive and decision-making capabilities. Therefore in this article, comprehensive knowledge has been provided about the Databases and the 10 best databases that are used in Machine Learning and Artificial Intelligence by developers in 2024.

What is a Database?

A database refers to the information that is set up for easily accessing, updating, and managing the data. Therefore databases are generally used for storing, accessing, and maintaining the sort of data. Databases are controlled by a DBMS. The database collects information on places, people, and things. This information is collected by the organizations so that it can be analyzed further for future reference.

10 Best Databases for Machine Learning(ML) & AI in 2024

There are various types of databases that are particularly used for machine learning and artificial intelligence in 2024. The top 10 Databases are mentioned below:

1. MongoDB

MongoDB is a popular database that is used in machine learning and artificial intelligence due to its scalability. It is a NoSQL database that was designed to scale horizontally and MongoDB also offers a wide range of advantages for machine learning and artificial intelligence technologies due to this reason MongoDB is a popular database among developers.

Key Features

  • It provides support to the aggregation tools and ad hoc queries.
  • Sharding, authentication, and replication are some of its key features.
  • MongoDB is useful for companies that are running large data applications.

2. Redis

Redis is another popular database that is used in machine learning and artificial intelligence. It offers a fast in-memory data store which is used as a distributed, in-memory key-value database. Redis also provides support to rich data types, atomic operations, and so on. It is an open-source database which is available for everyone to use.

Key Features

  • Redis provides a simple and easy-to-use interface.
  • It also consists of flexible data structures such as hashset, lists, strings, and so on.
  • Redis database is used to handle a large number of operations per second.

3. Couchbase

Couchbase is a type of NoSQL database that is used for machine learning and artificial intelligence. Couchbase is a distributed database which is one of the popular databases that supports plugins for the database secrets engine. This database helps in delivering unparalleled performance at scale and consists of multiple features.

Key Features

  • It consists of built-in big data and SQL integration.
  • Couchbase supports all cloud platforms.
  • This database offers caching, full-text search, and key-value store features.

4. HBase

HBase is a famous database management system that mainly offers managed clusters and also provides random and persistent access to the data. It is used for real-time analysis and tabular data. HBase also consists of a scalable big data store and also provides high-latency operations. With the help of HBase, it is easy to use the Java API for the customer’s access.

Key Features

  • HBase provides linear and modular scalability to the users.
  • It also provides automatic and configurable sharding of tables.
  • It also allows the data to be scaled all across the nodes as it is being stored in HDFS.

5. PostgreSQL

PostgreSQL is a type of relational database used for machine learning and artificial intelligence and this database is highly useful for developers who are looking to develop applications to protect data integrity. PostgreSQL is popular due to its speed and reliability. It is one of the stable databases which provides support to the different functions of SQL.

Key Features

6. Maria DB

Maria DB is a MySQL relational database management system that is used for multiple purposes such as e-commerce, enterprise-level features, and data warehousing. This database uses SQL to manage and query data. Maria DB is easy to set up, operate, and scale in the Cloud. With the help of Maria DB individuals can use various engines in one table.

Key Features

  • Maria DB offers higher query speed and it is more scalable.
  • It is one of the best databases for managing big data.
  • It also consists of a large range of plugins which makes this database more versatile.

7. MySQL

MySQL is a popular database used for machine learning and artificial intelligence and it also helps to manage the relational databases and performs multiple operations on the data. MySQL database mainly provides a set of commands for querying a database and retrieving the data. MySQL is one of the best databases for developers.

Key Features

  • It supports both structured data and semi-structured data.
  • Manipulation, transaction control, and data definition are some of its features.
  • This database is scalable and provides high performance to the users.

Elastic search is a famous database that is used for machine learning and artificial intelligence. This database allows the developers to search, store, and analyze large volumes of data quickly. Elastic search mainly offers a simple REST-based API and also uses schema-free JSON documents. It is mainly used for searching and log analysis.

Key Features

  • Elastic search allows efficient storage, retrieval, and management of both structured and unstructured data.
  • It is also used for providing real-time search results.
  • Elastic search is also used to provide high scalability and high performance to the users.

9. Amazon Dynamo DB

Amazon DynamoDB is another famous database that is used in artificial intelligence and machine learning to store an unlimited amount of data based on personalization needs. It is a fully managed serverless NoSQL database that consists of built-in security, restore, backup, and memory features. It offers built-in security, automated multi-region replication, and continuous backups.

Key Features

  • This database is highly scalable with customizable traffic filtering features.
  • It scales horizontally by expanding the single table over various servers.
  • Real-time processing features are available in Amazon Dynamo DB.

10. Microsoft SQL Server

Microsoft SQL Server is a relational database management system used in machine learning and artificial intelligence. It supports a large range of transaction processing, business intelligence, and so on. It is mainly useful for extracting the information from all the data by querying all across the structured and unstructured data.

Key Features

  • It includes a database engine, securing data, and multiple tools.
  • This database supports server-side scripting through R, Python, SQL, and so on.
  • Microsoft SQL Server enables the developers to work with the data storage to process them and provide the required results.

Conclusion

Databases are the main fundamentals which is used in machine learning and artificial intelligence and the databases are used by businesses to make informed business decisions. These databases are used to improve business processes and keep track of the customers. Therefore in this article, detailed knowledge has been provided about the databases and the top 10 databases used for machine learning and artificial intelligence in 2024.

FAQs on 10 Best Databases for Machine Learning & AI in 2024

What is the use of databases in machine learning and artificial intelligence?

The databases are the main components in machine learning and artificial intelligence as it helps to train the multiple machine learning and artificial intelligence models.

Name the top databases used in machine learning and artificial intelligence.

Some of the top databases which is used in machine learning and artificial intelligence are- MySQL, DynamoDB, Redis, Microsoft SQL Server, Elasticsearch, MongoDB, and so on.

What are some emerging database trends for ML/AI?

  • Cloud-based Databases
  • Hybrid Databases
  • In-memory Computing


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