Database Denormalization vs Materialized Views

Developers should use denormalization in scenarios where read performance is critical, such as in data warehousing, reporting systems, or high-traffic web applications where frequent joins slow down queries meets developers should use materialized views when dealing with slow, complex queries in read-heavy applications, such as reporting dashboards, data analytics, or caching frequently accessed data. Here's our take.

Database Denormalization

Nice Pick

Developers should use denormalization in scenarios where read performance is critical, such as in data warehousing, reporting systems, or high-traffic web applications where frequent joins slow down queries

Pros

• It is particularly useful for analytical queries that aggregate large datasets, as it reduces computational overhead by pre-combining data
• Related to: database-normalization, sql-optimization

Cons

• Specific tradeoffs depend on your use case

Materialized Views

Developers should use materialized views when dealing with slow, complex queries in read-heavy applications, such as reporting dashboards, data analytics, or caching frequently accessed data

Pros

• They are ideal for scenarios where real-time data is not critical, as they reduce database load and latency by serving precomputed results
• Related to: postgresql, oracle-database

Cons

• Specific tradeoffs depend on your use case

The Verdict

These tools serve different purposes. Database Denormalization is a concept while Materialized Views is a database. We picked Database Denormalization based on overall popularity, but your choice depends on what you're building.

The Bottom Line

Database Denormalization wins

Based on overall popularity. Database Denormalization is more widely used, but Materialized Views excels in its own space.