Denormalization

Denormalization is the process of intentionally introducing redundancy into a database by violating the principles of normalization. While normalization is crucial for eliminating data anomalies and ensuring data integrity, a highly normalized database can sometimes lead to performance issues. In a normalized design, data is spread across many small tables. Retrieving a complete set of information often requires performing complex joins across several tables, which can be computationally expensive and slow, especially for read-heavy applications like data warehousing and reporting. This is where denormalization comes in. By strategically combining tables or adding redundant data columns, we can reduce the number of joins needed for frequent queries, thereby improving query performance. For example, in an e-commerce application, you might store the `product_name` directly in the `Order_Items` table, even though it violates 3NF (as `product_name` depends on `product_id`). This avoids having to join with the `Products` table every time you want to display an order's details. Denormalization is a trade-off: you sacrifice some write efficiency and data integrity guarantees for a significant gain in read performance. It should be done carefully and only after a thorough analysis of query patterns and performance bottlenecks, as it makes the database schema more complex and increases the risk of data inconsistency.