Why might you need to denormalize your data model?

Denormalizing a data model is often necessary to enhance performance for read-heavy workloads. In scenarios where there are frequent read operations and less frequent write operations, a normalized database may require multiple joins across different tables to retrieve data. This can introduce latency and reduce performance. By denormalizing, you combine various related data into fewer tables. This reduces the complexity of queries and improves retrieval speed, allowing for faster access to the data essential for applications that rely on quick reads, such as analytics or reporting.

This need for improved performance becomes particularly significant as the volume of data grows, or when an application scales up to manage a higher number of users or transactions. In contexts where speed is critical, the trade-off of potentially having redundant data becomes acceptable because it greatly enhances user experience and system responsiveness.