10 Best Databases for Machine Learning & AI

Databases are fundamental to training all sorts of machine learning and artificial intelligence (AI) models. Over the last two decades, there has been an explosion of datasets available on the market, making it far more challenging to choose the right one for your tasks. At the same time, the larger number of datasets means you can find the perfect fit for whichever application you're aiming towards.

Here’s a list of the 10 best databases for machine learning & AI:

1. MySQL

Powered by Oracle, MySQL is one of the most popular databases on the market. Created in 1995, it has consistently been one of the top open-source relational database management systems (RDBMS) used by major companies like Facebook, Twitter, Uber, and Youtube.

What led to its rise in popularity? For one, MySQL offers enterprise-grade gestures and a free, flexible community license. It also has an upgraded commercial license and focuses on robustness and stability.

Here are some of the main advantages of MySQL:

• Data security layers to protect sensitive data.
• Scalability for when there are large amounts of data.
• Open source RDBMS with two separate licensing models.
• Multi-master ACID transactions through MySQL Cluster.
• Supports both structured data (SQL) and semi-structured data (JSON).

2. Apache Cassandra

Another top machine learning and AI database is Apache Cassandra, which is an open-source and highly scalable NoSQL database management system. Apache Cassandra was designed with the aim of processing massive amounts of data extremely quickly. The database is also used by big names like Instagram, Netflix, and Reddit.

Here are some of the main advantages of Apache Cassandra:

• Handles massive volumes of data.
• One of the most scalable databases with automatic sharding.
• Offers linear horizontal scaling.
• Decentralized database with multi-datacenter replication and automatic replication.
• Fault tolerant by automatically replicating data to multiple nodes.

3. PostgreSQL

PostgreSQL is one of the top open-source object-relational database systems. It extends the SQL language and combines it with various features to scale and safely store highly complicated data workloads. PostgreSQL is especially useful for developers looking to build applications or administrators looking to protect data integrity. It also helps create fault-tolerant environments.

Here are some of the main advantages of PostgreSQL:

• Highly secure with a robust access-control system.
• Offers ACID transactional guarantee.
• PostgreSQL extension Citus Data offers Distributed SQL features.
• Advanced indexes such as Partial Index and Bloom Filters.
• Supports structured data (SQL), semi-structured data (JSON, XML), key-value, and spatial data.

4. Couchbase

Couchbase is a document-focused engagement database that is also open-source and distributed. The server delivers great performance in any cloud and supports applications through its various capabilities, such as workload isolation, memory-first architecture, and geo-distributed deployments. It is able to maintain 99.999 availability and sub-millisecond latencies.

One of the main advantages of Couchbase is that the Couchbase Data Platform provides simple and powerful application development APIs across various programming languages, connectors, and tools. This makes it easy to build applications while also accelerating time to market.

Here are some of the main advantages of Couchbase:

• Includes built-in Big Data and SQL integration to allow users to leverage processing capacity, tools, and data.
• Supports all cloud platforms.
• Memory-first architecture enables fast and consistent experiences at scale.
• Offers security across the stack.

5. Elasticsearch

Another one of the top database choices, Elasticsearch is built on Apache Lucene. It is a distributed, open-source search and analyst engine that supports all types of data, such as numerical, textual, geospatial, structured, and unstructured.

Elasticsearch belongs to the Elastic Stack, which includes various open-source tools for enrichment, data ingestion, storage, visualization, and analysis.

Here are some of the main advantages of Elasticsearch:

• Many built-in features like data rollups and index lifecycle management for storing and searching data.
• Extremely efficient at full-text search.
• Useful for infrastructure monitoring, security analytics, and other security-related tasks.
• Horizontal scaling via automatic sharding.
• Part of the larger Elastic Stack that includes Elasticsearch, Kibana, Logstash, and Beats.

6. Redis

Redis is one of the most popular choices on the market. It is an open-source, in-memory data structure used as a database, message broker, and cache. One of the main features of Redis that draws customers is its support for various data structures like strings, sorted sets, bitmaps, geospatial indexes, hyperloglogs, and more. Redis also has Lua scripting, LRU eviction, built-in replication, transactions, and various levels of on-disk persistence.

Here are some of the main advantages of Redis:

• Automatic failover process.
• Redis-ML, which is a module that implements various machine learning models as built-in Redis data types.
• Variety of data structures like strings, lists, sets, hashes, bitmaps, streams, and more.
• Makes it easy to write complex code with fewer and simpler lines.

7. DynamoDB

A fully managed, multi-region database, Amazon DynamoDB features built-in security, in-memory cache, backup, and restore. The database’s popularity can be seen in the number of major companies that utilize it, such as AirBnB, Toyota, and Samsung. It carries out encryption at rest in order to reduce the complexity usually required for protecting sensitive data.

Two of the major benefits to DynamoDB are its scalability and data replication abilities. With virtual unlimited storage, you can store unlimited amounts of data based on personalized needs. When it comes to data items, they are all stored on SSDs. Replication is managed internally across different availability zones in a region, but it can also be made available across multiple regions.

Here are some of the main advantages to DynamoDB:

• Scales horizontally by expanding a single table over multiple servers.
• Highly secure with customizable traffic filtering, regulatory compliance automation, comprehensive database threat detection, and more.
• A fully managed service that doesn’t require hardware or software provisioning, software patching, distributed database cluster, or setup and configuration.

8. MLDB

The Machine Learning Database, or MLDB, is an open-source system aimed at tackling big data machine learning tasks. It can be used for data collection and storage through the training of machine learning models, or to deploy real-time prediction endpoints. MLDB is one of the easier datasets to use, since it provides a comprehensive implementation of the SQL SELECT statement. This means it treats datasets as tables, making it easier to learn and use for data analysts already versed in an existing Relational Database Management System (RDBMS).

Here are some of the main advantages of MLDB:

• Uses SQL as a mechanism to query data stored in the database.
• Training, modeling, and discovery process in MLDB has huge processing power.
• Supports vertical scaling with higher efficiency.

9. Microsoft SQL Server

The Microsoft SQL Server is a relational database management system (RDBMS) that is written in C and C++. It is especially useful for extracting insights from all the data by querying across relational, non-relational, structured, and unstructured data. It was the most popular commercial mid-range database in Windows Systems over the last 30 years, and it is currently one of the leading commercial database systems.

Here are some of the main advantages of Microsoft SQL Server:

• Offers ACID transactional guarantee.
• Supports server-side scripting through T-SQL, R, Python, Java, and .NET languages.
• Multi-model database that supports structured, semi-structured, and spatial data.

10. MongoDB

The last database on our list is MongoDB, which was released as the first document database in 2009. It was designed to specially handle document data, and it has been improved drastically over the last few years. MongoDB is currently the principal document database and the leading NoSQL database on the market. It provides a solution to the challenges of saving semi-structured data in the database.

Here are some of the main advantages of MongoDB:

• Horizontal scaling via auto-sharding.
• Built-in replication through primary-secondary nodes.
• Licenses including Community Server, Enterprise Server, and Atlas.
• Distributed multi-document ACID transactions with snapshot isolation.
• Full-text search engine and data lake built on MongoDB