Explain normalization and denormalization.

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Normalization and Denormalization are two important database design techniques used to organize data efficiently.

Normalization

Normalization is the process of organizing data in a database to reduce redundancy and improve data integrity. It involves dividing large tables into smaller, related tables and establishing relationships between them. The main goals are:

• Eliminate duplicate data.

• Ensure logical data storage.

• Maintain data consistency.

• Optimize update, insert, and delete operations.

For example, instead of storing a customer’s address in multiple order records, normalization would create a separate Customer table linked to Orders, ensuring the address is stored only once.

Normalization is done in forms (1NF, 2NF, 3NF, BCNF, etc.), each with stricter rules to refine data organization.

Denormalization

Denormalization is the opposite process, where normalized tables are combined or redundancy is intentionally introduced. This is usually done to improve performance, especially in systems where read queries are more frequent than updates.

For example, instead of keeping Customer and Orders as separate tables, denormalization may combine them into a single table, so queries can fetch all details in one step without multiple joins.

Key Difference

• Normalization → Focuses on minimizing redundancy and ensuring integrity. Best for transactional systems (OLTP).

• Denormalization → Focuses on improving query performance by reducing joins. Best for analytical systems (OLAP).

👉 In short: Normalization = efficiency in storage and accuracy, while Denormalization = efficiency in retrieval and speed.

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