[sahithya]

Normalization organizes data to reduce redundancy and improve integrity by dividing larger tables into smaller, linked ones based on dependencies. This minimizes storage space and update anomalies. While beneficial for data consistency (especially in transactional systems), it can lead to more complex queries with multiple joins, potentially impacting read performance.

Denormalization, conversely, intentionally adds redundancy by combining tables or duplicating data. This aims to optimize read performance by reducing the need for joins, simplifying queries, and speeding up data retrieval (common in analytical systems). However, it can increase storage requirements and the risk of data inconsistencies if not managed carefully. The choice depends on the specific needs of the application, balancing data integrity with query efficiency.