Normalization in databases refers to the process of organizing data in a database to reduce data redundancy and improve data integrity. It involves breaking down a large table into smaller, related tables, with each table representing a specific entity and its attributes.
Here's a breakdown of the concept:
* Data Redundancy: When the same data is stored in multiple places, it leads to redundancy, wasting storage space and increasing the risk of inconsistencies if data is updated in one place but not the others.
* Data Integrity: Normalization helps ensure that data is accurate, consistent, and reliable. It enforces constraints that prevent invalid data from being entered into the database.
* Database Efficiency: By reducing redundancy, normalization improves the efficiency of data retrieval and manipulation, leading to faster queries and updates.
Different Normal Forms:
Normalization is a process that progresses through different "normal forms," each with increasing levels of normalization:
* 1NF (First Normal Form): Each column contains atomic values (indivisible units of data), and there are no repeating groups of columns.
* 2NF (Second Normal Form): It must be in 1NF and all non-key attributes must be fully dependent on the primary key.
* 3NF (Third Normal Form): It must be in 2NF and all non-key attributes must be dependent only on the primary key, not on other non-key attributes.
Benefits of Normalization:
* Reduced data redundancy
* Improved data integrity
* Enhanced database performance
* Easier data maintenance and updates
Drawbacks of Normalization:
* Increased complexity in database design and implementation
* Potential performance overhead for complex queries involving joins across multiple tables
In summary: Normalization is a fundamental database design technique that helps to organize data effectively, reduce redundancy, improve integrity, and enhance efficiency. The level of normalization applied depends on the specific requirements of the database.