Normalization in DBMS is a process of organizing data in a database to minimize redundancy and improve data integrity. It involves structuring your database tables to efficiently store and retrieve data while reducing duplication.
Here's a breakdown of what normalization achieves:
Reduces Data Redundancy:
By eliminating repetitive data entries, normalization saves storage space and minimizes the chance of inconsistencies arising from the same information being stored in multiple places.
Improves Data Integrity:
Normalized tables ensure data dependencies are well-defined, meaning a change in one place is reflected accurately throughout the database. This reduces the risk of errors and maintains data consistency.
Enhances Data Manipulation:
Normalization simplifies inserting, updating, and deleting data. You can modify specific data points without affecting unrelated information in other parts of the database.
Normalization is achieved through a series of steps known as normal forms. These forms define progressive levels of data organization, with each level addressing specific data redundancy issues. Common normal forms include:
First Normal Form (1NF): The basic level that eliminates repeating groups within a table.
Second Normal Form (2NF): Ensures all non-key attributes depend on the entire primary key, not just a part of it.
Third Normal Form (3NF): Removes any dependencies between non-key attributes themselves, ensuring they solely depend on the primary key.
There are additional normal forms like Boyce-Codd Normal Form (BCNF) and Fourth Normal Form (4NF) that address more specific data dependency scenarios.
In conclusion, normalization is a crucial database design technique that promotes efficient data storage, retrieval, and manipulation by minimizing redundancy and ensuring data integrity.
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