What Is Database Normalization: Forms, Rules, & Examples

May 23, 2024
15 min read

While collecting large amounts of data from diverse sources may seem straightforward, the real challenge lies in processing this data to extract meaningful insights. The complexity of handling data from different sources, often varying in quality and structure, results in missing values, redundancy, and inconsistency. This is where data normalization can help. Normalizing your database can reduce data redundancy and improve data integrity, thus helping streamline data analysis for improved efficiency.

In this article, you'll explore the importance of database normalization and how to implement it effectively.

What Is Database Normalization?

Database normalization is the process of structuring data in a relational database to reduce redundancy and dependency. This is typically achieved by dividing a larger table into smaller, interrelated tables and defining relationships among them. The primary goal of normalization is to enhance data integrity and consistency by minimizing anomalies during data operations such as updates, insertions, and deletions.

However, it's important to note that normalization is not a one-size-fits-all solution; the extent of normalization depends on the specific requirements, complexity, and database size. While over-normalization can lead to performance issues, under-normalization can result in data integrity problems.

Why Should Database Be Normalized?

Normalization in databases is essential for creating a well-structured, efficient, and reliable system that ensures accurate and consistent data management. Let's explore how database normalization can enhance your database system.

Eliminates Data Redundancy: Redundancy occurs when the same information is stored in multiple locations within a database, leading to excess storage usage and potential data inconsistencies. Normalization organizes the data into separate tables according to logical relationships, reducing redundancy and ensuring that each data item is stored only once. This optimizes storage utilization and simplifies data maintenance.

Efficient Data Management: Normalization simplifies data management by organizing complex data into smaller, manageable units. Each table in a normalized database represents a specific entity or relationship, helping streamline data updates, deletions, and additions. This enhances the overall efficiency of data management processes, as you can access and modify relevant information more efficiently. 

Improves Query Performance: Well-normalized databases support efficient query execution, leading to faster data retrieval and enhanced performance. While normalization reduces the need for costly full-table scans by minimizing redundant data, consider balancing the degree of normalization to avoid complex queries from too many table joins.

Improves Data Integrity: Database management system normalization ensures data integrity by enforcing consistency and accuracy. This includes enforcing primary keys, foreign keys, and referential integrity, which prevents inconsistencies and preserves data quality.

How to Normalize Database?

Database normalization is guided by principles called "normal forms.” Let’s explore different types of database normalization rules with examples:

Normalize Database

Normalize Database

1 NF (First Normal Form)

1NF requires that the values in each column of a table are atomic; each column should contain only indivisible values. This prevents the storage of multiple values in a single column, laying the foundation for a structured database. Achieving 1NF is crucial for reducing redundancies, making the data more accessible, and simplifying data manipulation. It simplifies the database structure, making it easier to understand and maintain.


Let's consider a table storing information about students and their courses:

Student ID Student Name Course
1 Kate Java, SQL
2 Alice Python
3 Reena HTML

The table violates 1NF because the Course column contains multiple values separated by commas, and each cell does not hold a single atomic value.

Transforming to 1NF:

To adhere to 1NF, we must restructure the table to ensure each cell holds a single value. This can be achieved by separating the Course data into separate rows, as shown below.

Student ID Student Name Course
1 Kate Java
1 Kate SQL
2 Alice Python
3 Reena HTML

2 NF (Second Normal Form)

A database table is in 2NF if it is 1NF and all its non-key attributes are fully dependent on the primary key. Each non-key attribute must depend on the whole primary key, not just a part of it. 2NF is primarily concerned with eliminating partial dependencies, which occur when a column's value relies on only a portion of a composite primary key.


Let's consider a table that stores information about employees and their projects:

Employee ID Project ID Employee Name Project Name
1 101 John Project A
2 102 Alice Project B
3 103 Bob Project C

In this table,

EmployeeID and ProjectID together form a composite primary key.

EmployeeName and ProjectName are non-key attributes.

The non-key attribute EmployeeName depends only on the part of the primary key (EmployeeID), and ProjectName depends only on the part of the primary key (ProjectID).

Transforming to 2NF:

To adhere to 2NF, we need to separate the non-key attributes into separate tables, ensuring they are fully dependent on a primary key. 

Employees Table:
Employee ID Employee Name
1 John
2 Alice
3 Bob

Projects Table:
Project ID Project Name
101 Project A
102 Project B
103 Project C

Now, EmployeeName and ProjectName are fully dependent on the primary keys of their respective tables, aligning with 2NF requirements.

3 NF (Third Normal Form)

The 3NF is a level of database normalization that builds on the 1NF and 2NF by further addressing transitive dependencies between non-key attributes. It requires that every non-key attribute directly depends on a primary key and is not transitively dependent on another non-key attribute.

Let’s understand the concept of 3NF with an example table.:

Student ID Student Name Subject ID Subject
1X David 21 Java
2X Dolly 22 SQL
3X Nick 23 Python

The above table shows that Student ID determines Subject ID, and Subject ID determines Subject. Consequently, Student ID determines Subject through Subject ID, which indicates a transitive functional dependency.

Transforming to 3NF:

To bring the table to 3NF, we need to separate the attributes to remove the transitive dependency.

Students Table:
Student ID Student Name Subject ID
1X David 21
2X Dolly 22
3X Nick 23

Subjects Table:
Subject ID Subject
21 Java
22 SQL
23 Python

Now, Student ID and Subject ID each function independently. Each table is in 3NF, as there are no transitive dependencies between non-key attributes, hence satisfying 3NF requirements.

Boyce-Codd Normal Form (BCNF)

BCNF is an extension of the third normal form, designed to handle certain anomalies that 3NF does not address. To adhere to 3NF, a dataset must satisfy two conditions. First, it must already be in 3NF (no transitive dependencies). Second, for every non-trivial functional dependency, A→B, A must be a super key, a combination of attributes uniquely identifying each record in the table.

Let’s understand the concept of BCNF with an example:

Student ID Subject Professor
11 Python Kate
11 Java Mike
12 C# Alice
13 Python Bob
14 PHP John

This table shows that each student can take multiple subjects, and different professors can teach the same subject, like Python.

StudentID + Subject form a primary key together. 

Professor is a non-prime attribute.

As one professor teaches only one subject, we can use the Professor column to determine the subject (part of the primary key). Hence, you see a dependency here, where the subject is dependent on the professor, which is not a super key, hence violating BCNF.

Transforming to BCNF:

Let’s understand how we can divide the table to eliminate the dependency of the subject on a non-superkey:

Students Table:
Student ID Professor ID
11 101
11 102
12 103
13 104
14 105

Professors Table:
Professor ID Subject Professor
101 Python Kate
102 Java Mike
103 C# Alice
104 Python Bob
105 PHP John

As you can see, a new column, ProfessorID, has been created to remove the functional dependency of non-prime attributes. In the second table, ProfessorID is the super key, and all remaining columns are functionally dependent on it. Therefore, the Boyce-Codd normal form is satisfied

Streamline Data Normalization with Airbyte

Now, you understand that data normalization is vital for ensuring the consistency and reliability of your data. However, it can be a complex process, especially when dealing with data from diverse sources and in large volumes. Challenges such as varying data formats and inconsistencies are common. This is where data integration platforms like Airbyte can help you streamline the data collection and preparation for the normalization process.


Let’s explore the key features of Airbyte:

Custom Connectors: Airbyte offers an extensive catalog of over 350+ connectors that allow you to easily connect to various sources, extract data, and consolidate it into a centralized platform. If you don't find the desired connector in the list, Airbyte offers the flexibility to build custom ones using a Connector Development Kit (CDK).

Transformations: Airbyte allows seamless integration with dbt (data build tool) to perform advanced data transformations. By incorporating dbt into your data pipeline, you can ensure that the data is properly normalized and transformed according to your predefined rules and standards.

PyAirbyte: PyAirbyte's compatibility with various Python libraries, such as Pandas and SQL-based tools, provides a wide array of possibilities for data transformation. With PyAirbyte, you can quickly transform data into the desired format and prepare it for analysis.

Wrapping Up

Database normalization is an essential process for efficient data management. By adhering to the various database normal forms and following established rules, you can significantly streamline your data operations. The examples of normal forms in databases demonstrate how normalization can transform complex, unstructured data into organized, well-connected units, providing reliable information to make informed decisions.

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