denormalization

Denormalization is a database design technique in which information that would normally be stored across multiple normalized tables is combined or duplicated in a single table or across multiple tables. The primary goal is to improve read performance by reducing the number of joins required for queries and by providing precomputed or readily accessible data.

Denormalization is commonly used in read-heavy workloads such as data warehouses, business intelligence, reporting, and analytics, where fast query performance is essential. It is also employed in caching layers or application-level caches to reduce data retrieval latency and the complexity of certain queries. In some designs, denormalization appears in the form of flattened or wide tables and in precomputed aggregates or summaries.

Techniques include duplicating data from related tables (for example, storing a customer’s name and country alongside orders), adding derived attributes or precomputed aggregates, maintaining materialized views, and constructing star or snowflake schemas in data warehousing contexts. Other approaches include maintaining redundant foreign key references and maintaining separate copies of frequently read lookup data.

The main trade-off is between performance and data integrity. Denormalization can significantly speed up reads and simplify query patterns but increases the risk of data anomalies and inconsistency during updates, inserts, or deletes. It typically requires additional mechanisms to keep redundant data synchronized, such as triggers, application logic, or periodic refreshes of materialized views, which can add maintenance overhead.

When to use denormalization depends on the workload and requirements. It is often appropriate after identifying join-related bottlenecks, in systems with predictable read patterns, or in data marts and reporting stores where fast access to aggregated data is prioritized. Normalization and denormalization are often used in complementary ways.