What do you understand by normalization and denormalization?

Data Science, AI and ML
#1

In databases, normalization and denormalization are the two most used approaches.

  • By arranging fields and tables in databases, normalization helps to reduce data redundancy and dependence. It entails creating tables and establishing links between them according to predetermined principles. These principles may be used to reduce duplication and inconsistent dependencies, making them more adaptable.

  • Denormalization is the polar opposite of normalization. To speed up complicated searches involving several tables to join, we simply inject redundant data. By adding redundant data or clustering the data, we may improve the read speed of a database.

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#2
  1. Normalization is the technique of dividing the data into multiple tables to reduce data redundancy and inconsistency and to achieve data integrity. On the other hand, Denormalization is the technique of combining the data into a single table to make data retrieval faster.
  2. Normalization is used in OLTP system, which emphasizes on making the insert, delete and update anomalies faster. As against, Denormalization is used in OLAP system, which emphasizes on making the search and analysis faster.
  3. Data integrity is maintained in normalization process while in denormalization data integrity harder to retain.
  4. Redundant data is eliminated when normalization is performed whereas denormalization increases the redundant data.
  5. Normalization increases the number of tables and joins. In contrast, denormalization reduces the number of tables and join.
  6. Disk space is wasted in denormalization because same data is stored in different places. On the contrary, disk space is optimized in a normalized table.