Relational databases work best for structured data and have a predefined schema. They are table-based and more rigid than non-relational databases. Examples: MySQL, Oracle, PostgreSQL, MS SQL Server.

Non-relational databases work best for unstructured data and have a dynamic schema. They’re document-based or event key-value-based and more flexible than relational databases. Examples: MongoDB, Redis, Cassandra, Firebase.

A non-relational open-source database management system designed to handle high volumes of data. This advantage makes Cassandra a great database for big companies with distributed data centers like Facebook, Instagram, Apple, Netflix, Uber, etc.

A non-relational key-value database service that’s a part of Amazon Web Services (AWS). It is a great choice for large, high-performance apps as it provides nearly infinite global scaling with milliseconds latency.

A non-relational, cloud-hosted, real-time, Google-owned database for storing and syncing unstructured data. Because changes are instantly synchronized across all connected clients, Firebase is great for building cross-platform (iOS, Android, web, C++, Unity) applications. It also allows apps to work offline.

An advanced relational data management and analytics system that’s great for low-latency transactions and massive workloads. Developed by the pioneer of data management, IBM Db2 is reliable and scalable. It runs on Linux, Unix, and Windows operating systems.

A relational database management system with several options that serve applications of every size. But because of its complicated and high pricing, analytical and business features, MS SQL is most popular with enterprises. And since it is a product of Microsoft, MS SQL is compatible with MS Azure, Microsoft Cloud, and others.

An open-source, non-relational document-based database. Because the database stores query data in RAM, MongoDB allows accessing, retrieving, and multiplying data quickly, which is essential if an application requires speed. MongoDB is also a preferred choice for all sorts of catalogs and real-time applications.

An easy-to-use relational database with a mild learning curve. MySQL is the number one web database and is supported by leading cloud platforms. It may not be the most scalable database, though, so MySQL works best for smaller web-based applications, OLAP/OLTP systems, and BI tools.

A hybrid cloud relational database management system that supports different kinds of data models (document, graph, key-value, etc.). Owned by Oracle Corporation, the database can boast different applications and tools available and a hefty price tag. It’s comfortable with processing large amounts of information and numerous databases, has excellent support but a steep learning curve.

An open-source relational database for complex data processing. PostgreSQL is extremely scalable, supports various data types out of the box, and is compatible with helpful tools and extensions. While it’s a universal data management system, PostgreSQL is widely used for data warehousing and analytics. And thanks to the ability to process giant loads of data, the database is great for financial applications.

A non-relational database that is frequently used as a cache. The amounts and speed of data processing with Redis are impressive. All thanks to its in-memory data store and no dependencies. This makes Redis great for IoT applications with tons of data that has to be processed quickly.

A lightweight, portable, serverless relational database engine. Because it’s so light, SQLite can run as embedded software (in smart TVs, electronic devices, etc.). It’s reliable, doesn’t require installation, and is easy to learn. With excellent performance and compatibility with many third-party tools, SQLite is great for version control, CAD, and financial systems.