Data Denormalization
Data denormalization is a database optimization technique that involves intentionally adding redundant data to one or more tables to improve read performance and simplify queries. It reduces the need for complex joins by storing related data together, which can speed up data retrieval in read-heavy applications. This approach contrasts with normalization, which minimizes redundancy to ensure data integrity and reduce anomalies.
Developers should use data denormalization in scenarios where read performance is critical, such as in data warehouses, reporting systems, or high-traffic web applications where frequent joins slow down queries. It is particularly useful for analytical workloads, caching layers, or NoSQL databases like MongoDB, where denormalized schemas are common to support fast access patterns. However, it should be applied judiciously, as it can lead to data inconsistency and increased storage costs.