What does it mean to denormalize data in Cassandra?

Denormalizing data in Cassandra primarily refers to the practice of structuring the data in such a way that it minimizes the need for JOIN operations. In a typical relational database, data is often normalized to remove redundancy and ensure data integrity, which usually involves splitting data across multiple related tables. This normalization can lead to complex queries that require JOINs to gather related data.

Cassandra, being a NoSQL database, takes a different approach. Since it is designed for high availability and scalability, it encourages a denormalized data model. In this model, data is often duplicated across multiple tables or written in a way that it groups related information together. This design enables simpler and more efficient read operations because all the necessary data can be retrieved with fewer queries and without the overhead of performing JOINs, which are not natively supported in Cassandra.

By denormalizing data, you improve performance and speed for read operations. In scenarios where applications require quick access to data without the complexity of combining data from disparate sources, denormalization proves especially effective. Thus, the choice related to reducing the need for JOIN operations accurately captures the essence of what denormalization means in the context of Cassandra.