Data normalization is a method in which data attributes of a data model are sorted to enhance the cohesion of entity types and to reduce and even eliminate data redundancy.
Normalization of a relational database consists of applying a series of rules to avoid future queries or unnecessarily complex queries, to eliminate redundancies and inconsistencies of dependence in the design of tables.
There are a series of guidelines to make sure that databases are normalized, which are referred to as standard forms and are numbered from one through five. Generally, databases are normalized using 1NF, 2NF, and 3NF, and occasionally with 4NF. The 5NF form is very rarely seen.
These are guidelines and guidelines only. Occasionally, it becomes necessary to swerve from them to satisfy practical business requirements. However, when variations take place, it’s essential to assess any possible ramifications they could have on your system and account for potential inconsistencies.
Normalization rules are divided into the following typical forms:
First Normal Form (1NF)
For a table to be in the First Normal Form, it must comply with the following rules:
It should only have atomic valued attributes/columns, with unique names.
The values that are stored in a column should be of the same domain. The order in which data is stored makes no difference.
Second Normal Form (2NF)
The second normal form is the second step in normalizing a database. 2NF builds on the first normal form.
Third Normal Form (3NF)
It builds on the first and second normal forms and it should not have Transitive Dependency.
Boyce and Codd Normal Form (BCNF)
Boyce and Codd’s Normal Form is a higher version of the Third Normal form, which deals with a specific type of anomaly that is not handled by 3NF.
Fourth Normal Form (4NF)
For a table to be in the Fourth Normal Form it should be in the Boyce-Codd Normal Form and it should not have Multi-Valued Dependency.
Why is Data Normalization Important?
There are two main benefits of having a highly normalized data schema:
- Improved consistency. Information is stored only in one place, reducing the possibility of inconsistent data.
- Easier object-to-data mapping. Highly-normalized data schemas, in general, are closer conceptually to object-oriented schemas because the object-oriented goals of promoting high cohesion and loose coupling between classes result in similar solutions (at least from a data point of view).
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