Data Normalization:

1. Definition: Data normalization is the process of organizing and structuring data in a relational database to eliminate redundancy and ensure data integrity.

2. Purpose: The goal of data normalization is to minimize data duplication, improve data consistency, and simplify database maintenance and updates.

3. Normal Forms:

   • First Normal Form (1NF): Ensures that each column in a table contains atomic values and there are no repeating groups.

   • Second Normal Form (2NF): Builds upon 1NF and eliminates partial dependencies by ensuring that non-key attributes are dependent on the entire primary key.

   • Third Normal Form (3NF): Builds upon 2NF and eliminates transitive dependencies by ensuring that non-key attributes are dependent only on the primary key.

   • Boyce-Codd Normal Form (BCNF): A stronger version of 3NF that eliminates all non-trivial functional dependencies.

   • Fourth Normal Form (4NF) and Fifth Normal Form (5NF): Address more complex dependencies, such as multivalued and join dependencies, respectively.

4. Benefits of Data Normalization:

   • Eliminates data redundancy, leading to improved storage efficiency.

   • Reduces the likelihood of data inconsistencies and update anomalies.

   • Simplifies database design and improves maintainability.

   • Enhances data integrity and accuracy.

   • Supports better query performance in some cases.

Data Denormalization:

1. Definition: Data denormalization is the process of intentionally introducing redundancy into a normalized database structure to optimize performance.

2. Purpose: Denormalization aims to improve query performance by reducing the number of joins and improving data retrieval speed.

3. Techniques for Denormalization:

   • Combining tables: Merging multiple related tables into a single table to minimize joins.

   • Adding redundant columns: Storing duplicated data in different tables to avoid complex joins.

   • Precomputing derived data: Calculating and storing derived data in advance to eliminate the need for complex calculations during queries.

4. Considerations for Data Denormalization:

   • Denormalization introduces redundancy, which can lead to increased storage requirements.

   • It can complicate data updates and maintenance, as changes need to be propagated to multiple redundant copies.

   • Careful planning and analysis are required to determine which parts of the database can benefit from denormalization.

   • Denormalization is typically applied in read-heavy systems or for specific performance optimization needs.