Normalization is a database design technique used to organize and structure relational databases in a way that minimizes data redundancy and ensures data integrity. The goal of normalization is to eliminate or reduce data anomalies, anomalies that can arise when the same data is stored in multiple places within a database. The process involves breaking down large tables into smaller, related tables and applying rules to ensure data consistency and maintain referential integrity.
There are several levels of normalization, each represented by a normal form (NF). The most commonly used normal forms are:
First Normal Form (1NF):
- Ensures that each column in a table contains only atomic values (indivisible and not further decomposable).
- Eliminates repeating groups by separating them into new tables.
Second Normal Form (2NF):
- Requires that a table is in 1NF.
- Ensures that each non-key attribute is fully functionally dependent on the primary key.
- Handles partial dependencies by creating separate tables for related attributes.
Third Normal Form (3NF):
- Requires that a table is in 2NF.
- Eliminates transitive dependencies, where an attribute depends on another non-key attribute.
- Achieves this by creating new tables for the attributes causing the transitive dependencies.
Boyce-Codd Normal Form (BCNF):
- A stricter form of 3NF, focused on addressing more complex types of functional dependencies.
- Requires that every determinant (candidate key) determines every other non-key attribute.
Normalization helps in maintaining data integrity and reducing redundancy, which in turn contributes to better data quality and easier maintenance. However, excessive normalization can lead to complex database structures and potentially slower query performance due to the need for joins.
It’s important to strike a balance between normalization and denormalization based on the specific needs of the application and the trade-offs between storage efficiency, data consistency, and query performance.