What are the different techniques used for denormalization in database normalization?

There are several techniques used for denormalization in database normalization, including:

1. Materialized Views: This technique involves creating precomputed views that store the results of complex queries. By storing the computed results, it reduces the need for joining multiple tables and improves query performance.

2. Horizontal Denormalization: In this technique, data from multiple tables with a one-to-one or one-to-many relationship is combined into a single table. This reduces the need for joining tables and simplifies queries, but it can lead to data redundancy.

3. Vertical Denormalization: This technique involves splitting a table with many columns into multiple tables, each containing a subset of the columns. This can improve query performance by reducing the amount of data that needs to be read, but it can also increase the complexity of queries.

4. Database Partitioning: This technique involves dividing a large database into smaller, more manageable partitions. Each partition can be stored on a separate physical device, improving query performance by allowing parallel processing.

5. Caching: Caching involves storing frequently accessed data in memory to improve query performance. This can be done at the application level or by using database-specific caching mechanisms.

It is important to note that while denormalization can improve query performance, it can also introduce data redundancy and increase the complexity of maintaining data integrity. Therefore, it should be used judiciously and with careful consideration of the specific requirements and trade-offs involved.