Table of contents
Data Normalization:
Definition: Data normalization is the process of organizing and structuring data in a relational database to eliminate redundancy and ensure data integrity.
Purpose: The goal of data normalization is to minimize data duplication, improve data consistency, and simplify database maintenance and updates.
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.
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:
Definition: Data denormalization is the process of intentionally introducing redundancy into a normalized database structure to optimize performance.
Purpose: Denormalization aims to improve query performance by reducing the number of joins and improving data retrieval speed.
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.
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.