Data Normalization

Data normalization is a process used to organize a database in such a way that reduces redundancy and dependency by dividing large tables into smaller ones and defining relationships between them. This helps improve data integrity and efficiency in querying and updating data.

Why is data normalization important?

There are several reasons why data normalization is important:

• Eliminate Redundancy: By breaking down tables into smaller ones, redundant data is minimized, which helps in saving storage space and reduces the chances of inconsistencies.
• Reduce Data Anomalies: Normalized data reduces the chances of insertion, update, and deletion anomalies that can occur when data is not properly organized.
• Improve Data Integrity: With normalized data, there is a higher level of data integrity as updates and modifications are made in one place, reducing the risk of inconsistencies.
• Enhance Query Performance: Normalized data allows for faster query performance as smaller tables are easier to search and retrieve data from, leading to improved efficiency.

Normalization Forms

There are different normal forms that databases can be normalized to, each with its own set of rules and requirements. The common normalization forms include:

• First Normal Form (1NF): In 1NF, each column in a table must contain atomic values, and there should be no repeating groups of columns or data.
• Second Normal Form (2NF): To be in 2NF, a table must first be in 1NF and every non-key column should be fully functionally dependent on the primary key.
• Third Normal Form (3NF): 3NF requires a table to be in 2NF and every non-key column should be dependent on the primary key, not on any other non-key column.
• Boyce-Codd Normal Form (BCNF): BCNF is an advanced form of normalization where every determinant is a candidate key. It eliminates all anomalies related to functional dependencies.

Steps in Data Normalization

The process of normalizing data typically involves the following steps:

1. Identify the attributes: Determine the attributes or columns in each table and the relationships between them.
2. Eliminate redundant data: Remove any repeating groups or redundant data by breaking down tables into smaller ones.
3. Create separate tables: Create separate tables for related data, ensuring each table has a unique primary key.
4. Define relationships: Define relationships between tables using primary and foreign keys to maintain data integrity.
5. Normalize to the desired form: Normalize the tables to the desired normal form, such as 1NF, 2NF, 3NF, or BCNF.

Example of Data Normalization

Consider a database for a library with the following unnormalized table:

By normalizing this table, we can create separate tables for books, authors, and genres, resulting in a more efficient and organized database structure.