denormalizasyon

Denormalization is the deliberate introduction of redundancy into a database schema by merging related data into fewer tables or duplicating data across tables to reduce the need for joins and improve read performance. It is the reverse of normalization, which aims to minimize duplication and preserve data integrity through well-defined dependencies.

Denormalization is typically used in read-heavy systems, data warehouses, and reporting workloads where fast query responses are more important than write efficiency. By reducing joins and simplifying queries, it can speed up scans, aggregations, and user-facing dashboards.

Common techniques include duplicating frequently accessed fields across related records, consolidating related entities into a single table, and maintaining derived attributes such as totals in a separate denormalized structure or materialized view. Caching or precomputed aggregates are related approaches that serve similar goals.

The main trade-off is data redundancy, which increases storage and the risk of inconsistencies. Updates must be propagated to all copies, which can complicate write operations and integrity constraints. Denormalization requires careful design, governance, and often automated synchronization mechanisms such as triggers or application logic.

Typical use cases include an orders table that also stores customer_name for quick ordering lookups, or a fact table with precomputed daily totals to support rapid reporting. It is common in data warehouses, analytics workloads, and certain NoSQL or distributed SQL environments where read throughput matters.

Denormalization should be applied selectively and documented as part of the data model. It is usually pursued only after profiling and when the performance benefits justify the added maintenance burden.