Database normalization is a process used in designing a relational database to reduce data redundancy and dependency by organizing fields and table of a database. The normalization process involves breaking down large tables into smaller, related tables, which helps in reducing data redundancy and improving data integrity. There are several normal forms, each building on the previous one. The most common normal forms are:

1. First Normal Form (1NF):

• Eliminate duplicate columns from the same table.
• Create a separate table for each group of related data.
• Identify a unique column or set of columns (the primary key) for each table.

2. Second Normal Form (2NF):

• Meet all the requirements of 1NF.
• Remove subsets of data that apply to multiple rows of a table and place them in separate tables.
• Create relationships between these new tables and their predecessors using foreign keys.

3. Third Normal Form (3NF):

• Meet all the requirements of 2NF.
• Remove columns that are not dependent on the primary key.
• Create separate tables for independent data, and establish relationships with foreign keys.

4. Boyce-Codd Normal Form (BCNF):

• Meet all the requirements of 3NF.
• Every determinant (attribute that uniquely determines another attribute) must be a candidate key.

5. Fourth Normal Form (4NF):

• Meet all the requirements of BCNF.
• Handle multi-valued dependencies by creating a separate table for each multi-valued dependency

6. Fifth Normal Form (5NF):

1. Meet all the requirements of 4NF.
2. Address cases where information can be derived from other information in the database.

normalization process with a hypothetical scenario and examples:

Scenario:

Consider a database for a library system where information about books, authors, and publishers is stored. The initial design has a single table called Books with the following attributes:

• BookID (Primary Key)
• Title
• Author
• AuthorBio
• Publisher
• PublishedYear

Example in First Normal Form (1NF):

In 1NF, we want to eliminate duplicate columns and have a unique identifier for each row. The initial table is already in 1NF.

Books
-----------------------------------------
| BookID | Title     | Author  | ...     |
-----------------------------------------
| 1      | Book1     | Author1 | ...     |
| 2      | Book2     | Author2 | ...     |
| 3      | Book3     | Author3 | ...     |
-----------------------------------------

Example in Second Normal Form (2NF):

In 2NF, we want to remove partial dependencies by creating separate tables for related data. Let’s decompose the table into Books and Authors.

Books
------------------------
| BookID | Title | ... |
------------------------
| 1      | Book1 | ... |
| 2      | Book2 | ... |
| 3      | Book3 | ... |
------------------------

Authors
---------------------
| Author  | AuthorBio |
---------------------
| Author1 | Bio1      |
| Author2 | Bio2      |
| Author3 | Bio3      |
---------------------

Example in Third Normal Form (3NF):

In 3NF, we want to remove columns not dependent on the primary key. Let’s further decompose the Books table.

Books
------------------------
| BookID | Title | ... |
------------------------
| 1 | Book1 | ... |
| 2 | Book2 | ... |
| 3 | Book3 | ... |
------------------------

Authors
---------------------
| Author | AuthorBio |
---------------------
| Author1 | Bio1 |
| Author2 | Bio2 |
| Author3 | Bio3 |
---------------------

Publishers
--------------------
| Publisher | Year |
--------------------
| Pub1 | 2000 |
| Pub2 | 2005 |
| Pub3 | 2010 |
--------------------


Now, the data is in 3NF. The Publishers table contains information dependent only on the Publisher column.

This process would continue with higher normal forms based on specific requirements and dependencies in the data. Remember, the level of normalization applied depends on the specific needs of the application and database.

Each successive normal form builds upon the previous ones, and the choice of which level of normalization to achieve depends on the specific requirements of the database and the nature of the data being stored. It’s important to strike a balance between normalization and performance, as highly normalized databases may require more complex queries and can sometimes result in performance issues.

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