DENORMALIZATION
DENORMALIZATION is a database optimization technique used to improve the read performance of a RELATIONAL-DATABASE. In a standard database design, NORMALIZATION is applied to minimize DATA-REDUNDANCY and ensure DATA-INTEGRITY by organizing data into multiple related tables. However, as the volume of data grows, the JOIN-OPERATIONS required to retrieve information from these fragmented tables can become computationally expensive.
According to documentation from IBM, DENORMALIZATION involves intentionally introducing redundancy by merging tables or adding repeated data fields. This approach is highly prevalent in DATA-WAREHOUSING and OLAP (Online Analytical Processing) systems, where query speed is prioritized over the efficiency of data modifications. By reducing the number of joins, the SQL-ENGINE can fetch results significantly faster.
While the benefits include faster data retrieval and simplified queries, there are notable trade-offs. As highlighted by Microsoft Azure Architecture Center, denormalized data requires more storage space and complicates the process of updating records. Developers must often implement DATABASE-TRIGGERS or complex application logic to maintain consistency across redundant fields. Common strategies for implementation include the use of MATERIALIZED-VIEWS, storing derived values like totals, and duplicating foreign key attributes for direct access.