What is Normalization?
Normalization is the process of structuring a database to eliminate redundancy and ensure data integrity. It follows a set of rules (normal forms) to minimize duplication and improve consistency in relational databases.
What is Denormalization?
Denormalization is the opposite of normalization, where data redundancy is intentionally introduced to improve performance. It is used when read operations need to be faster at the cost of increased storage and data maintenance complexity normalized vs denormalized.
Key Differences Between Normalization and Denormalization
Data Redundancy: Normalization removes duplication, while denormalization introduces redundancy to optimize performance.
Query Performance: Normalized databases require complex joins, which can slow down queries, while denormalized databases allow faster reads but require more storage.
Data Integrity: Normalization ensures data consistency, while denormalization increases the risk of inconsistencies due to duplicated data.
Storage Requirements: Normalization reduces storage consumption, whereas denormalization increases storage due to redundant data.
When to Use Normalization?
When data integrity is the top priority.
In transaction-heavy applications like banking and e-commerce.
When minimizing storage is important.
When to Use Denormalization?
In read-heavy applications like reporting dashboards and analytics.
When optimizing performance over storage efficiency.
In big data applications where fast retrieval is critical.
Conclusion
Both normalization and denormalization have their advantages and are used based on the specific needs of an application. While normalization ensures data consistency and efficiency, denormalization boosts read performance at the cost of redundancy. The choice depends on whether performance or data integrity is the priority.