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:
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).
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.