Denormalization
Denormalization is a database optimization technique that involves intentionally adding redundant data to one or more tables to improve read performance and reduce the complexity of queries. It is commonly used in relational databases to avoid expensive join operations by storing data in a non-normalized form, which can speed up data retrieval at the cost of increased storage and potential data inconsistency. This approach is often applied in data warehousing, reporting systems, or high-read environments where query speed is prioritized over data integrity.
Developers should use denormalization when dealing with read-heavy applications, such as analytics dashboards, reporting tools, or e-commerce platforms, where fast data retrieval is critical and write operations are less frequent. It is particularly useful in scenarios where complex joins slow down performance, as it simplifies queries by pre-combining related data into a single table. However, it should be applied judiciously, as it can lead to data redundancy, update anomalies, and increased maintenance overhead, making it less suitable for transactional systems with frequent writes.