What is the purpose of denormalization in a database?

Denormalization is used to improve query performance in relational databases. It involves deliberately deviating from normal forms by redundant data storage or table merging to reduce the number of joins during data retrieval.

Main reasons for applying denormalization:

• Speeding up reads: Fewer JOIN operations significantly accelerate query execution, especially with large data volumes.
• Simplifying queries: Queries can become simpler and shorter as multi-step joins are reduced.
• Preparing data for reporting: Denormalized structures are often used for building data marts or aggregated tables optimized for analytical queries.
• Hardware platform considerations: Sometimes, denormalization helps optimize disk space usage and I/O operations.

Disadvantages of denormalization:

• Data redundancy: The same data is stored in multiple places, increasing database size.
• Complexity of updates and deletes: Changing or deleting data requires modifications in multiple tables simultaneously, complicating logic and increasing the risk of data inconsistency.
• Data integrity violations: The likelihood of anomalies increases during insert, update, or delete operations due to redundancy.
• Design and maintenance complexity: The database schema becomes less intuitive and harder to support.

Denormalization is applied selectively and should be carefully considered based on performance analysis of specific queries and balancing read speed against write/maintenance complexity.