What are the best database options for a scalable application?

1 Relational databases

Relational databases store data in tables, which are organized by rows and columns. Each row represents an entity, such as a user or a product, and each column represents an attribute, such as a name or a price. Relational databases use a structured query language (SQL) to manipulate and retrieve data. Some examples of relational databases are MySQL, PostgreSQL, and Oracle.

Relational databases are good for applications that require consistency, integrity, and accuracy of data. They also support complex queries and transactions, which are useful for analytical and financial applications. However, relational databases have some drawbacks for scalability. They can be difficult to scale horizontally, which means adding more servers to distribute the load. They also have a rigid schema, which means changing the structure of the data can be costly and time-consuming.

2 Non-relational databases

Non-relational databases store data in various formats, such as key-value pairs, documents, graphs, or columns. They do not follow a predefined schema, which means they can store different types of data in the same collection. Non-relational databases use a variety of query languages, depending on the data model. Some examples of non-relational databases are MongoDB, Redis, and Cassandra.

Non-relational databases are good for applications that require flexibility, performance, and scalability. They can handle large volumes of unstructured or semi-structured data, such as social media posts, images, or videos. They can also scale horizontally, which means they can easily add more servers to handle more traffic. However, non-relational databases have some drawbacks for reliability. They may not guarantee consistency, durability, or isolation of data, which can lead to data loss or corruption. They also have limited support for complex queries and transactions, which can limit the functionality of some applications.

3 Hybrid databases

Hybrid databases combine the features of both relational and non-relational databases. They can store data in different formats, depending on the use case, and provide a unified interface to access and manipulate them. Hybrid databases aim to balance the trade-offs between consistency and scalability, as well as between structure and flexibility. Some examples of hybrid databases are Amazon Aurora, Google Spanner, and CockroachDB.

Hybrid databases are good for applications that need both the advantages of relational and non-relational databases. They can handle diverse and dynamic data, as well as complex and transactional operations. They can also scale horizontally and vertically, which means they can adjust the number and size of servers according to the demand. However, hybrid databases have some challenges for complexity and compatibility. They can be difficult to set up and maintain, as they require more resources and expertise. They also may not work well with some existing tools and frameworks, which can affect the integration and migration of applications.