In what cases is data normalization useful, and in which is it harmful? When can normalization be neglected?

Data normalization is useful when you need to minimize redundancy and ensure data integrity, especially in transactional systems. It simplifies updates and prevents anomalies when data changes.

However, normalization can be harmful in systems with high read performance requirements, such as analytical warehouses or real-time systems, where data redundancy and denormalization speed up access.

Normalization can be neglected if:

• The main load is read operations, and response speed is crucial.
• Data is rarely updated, and redundancy does not cause problems.
• The system architecture includes data caching or replication.

Thus, the choice depends on specific system requirements and the balance between integrity and performance.