Normalization vs denormalization? #viralshorts2023

Normalization:

A process of organizing data in a database. It involves breaking down a table into smaller tables and connecting them using relationships.

Objective:

Minimize redundancy and dependency by organizing data into separate, related tables.

Benefits:

Reduces data duplication, which saves storage space.
Improves data integrity by minimizing anomalies.

Drawbacks:

1.Can lead to complex queries when retrieving data spread across multiple tables.
2.May require more joins, which can impact performance.

Use Cases:

Transaction processing systems where data integrity is critical.
OLTP (Online Transaction Processing) databases.

Denormalization:

The process of adding redundancy to a database. It involves combining tables or adding redundant data to speed up queries.

Objective:

Improve query performance by reducing the need for complex joins.

Benefits:

Faster query performance as data is often in a more consolidated form.
Simplifies complex queries by reducing the number of joins.

Drawbacks:

Increases storage requirements due to redundancy.
May lead to data integrity issues if not managed carefully.

Use Cases:

Data warehousing where reporting and analytics are the primary focus.
OLAP (Online Analytical Processing) databases.
When to Use:

Normalization is generally favored for transactional systems where data integrity and consistency are paramount.

Denormalization is preferred for reporting and analytical systems where read performance is critical and redundancy is acceptable.

Remember, the choice between normalization and denormalization should be based on the specific requirements and use cases of the database. Often, a combination of both techniques is employed to strike the right balance.

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