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⇱ Normalization vs. Denormalization - GeeksforGeeks

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Normalization vs. Denormalization

Last Updated : 31 Jan, 2026

Normalisation and denormalisation are used to alter the structure of a database. The key difference is that normalisation reduces redundancy by organising data into smaller, well-structured tables, while denormalisation intentionally introduces redundancy by merging tables to speed up query performance.

Normalisation

Normalisation is the method used in a database to reduce data redundancy and data inconsistency in the table. It is the technique in which non-redundant and consistent data are stored in a set schema. By using normalisation, the number of tables is increased instead of decreased.

Benefits

  • Data is reduced in the table.
  • Optimized memory.
  • Maintain data integrity.

Drawbacks

  • The number of tables is increased.
  • Consume more resources when using expensive operations.
  • Query performance may decrease due to multiple table joins.
👁 before_normalization
Example of Normalization


Denormalization

Denormalization is also the method which is used in a database. It is used to add the redundancy to execute the query quickly. It is a technique in which data are combined to execute the query quickly. By using denormalization the number of tables is decreased which oppose to the normalization.

Benefits

  • Execute the query quickly.
  • Fewer joins are needed, so queries execute faster.
  • Improves read performance for complex queries.

Drawbacks

  • Wastage of memory because store the duplicate data.
  • Complex data maintenance occurs because INSERT, UPDATE, and DELETE operations become harder due to data redundancy.
  • Does not maintain data integrity.
👁 before_denormalization
Example of Denormalization


Normalization and Denormalization

Normalization

Denormalization

It stores non-redundant and consistent data in a structured schema.

It combines data from multiple tables to execute queries faster.

Data redundancy and inconsistency are minimized.

Data redundancy is intentionally added for faster query execution.

Data integrity is maintained .

Data integrity is not maintained .

Redundancy is reduced or eliminated.

Redundancy is added instead of being eliminated.

The number of tables increases.

The number of tables decreases.

Disk space usage is optimized.

Disk space usage is not optimized.

Query execution may be slower due to joins.

Query execution is faster due to fewer joins.

Used in OLTP systems where accuracy and consistency are important.

Used in OLAP systems where quick data retrieval and fast query responses are required.

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