Understanding First Normal Form (1NF) in DBMS

Summary: First Normal Form (1NF) ensures structured databases by eliminating duplicate columns, enforcing atomicity, and using unique identifiers. It enhances data consistency and retrieval. Understanding 1NF is crucial for database optimisation. Learn SQL and data science techniques with Pickl.AI’s courses to master efficient data management and analytics.

Introduction

Databases are like digital filing cabinets, but they can become a mess without proper organisation! That’s where database normalisation helps. The first normal form in DBMS (1NF) ensures data is stored neatly.

Why care? The DBMS market, worth $63.5B in 2022, is expected to reach $152.36B by 2030 (growing 11.56% annually). With data booming, structured databases are a must!

In this blog, we’ll cover first normal form rules, easy first normal form examples, and why 1NF is crucial. Even if you’re new to databases, don’t worry—we’ll keep it simple!

Key Takeaways

• 1NF ensures atomicity, meaning each field contains a single, indivisible value.
• Eliminates duplicate columns, ensuring a structured and organised database.
• Requires a unique identifier, ensuring each row is uniquely identified by a primary key.
• Improves data consistency by reducing redundancy and enhancing data retrieval.
• Learn SQL for better DBMS skills with Pickl.AI’s data science courses.

What is the First Normal Form (1NF)?

First Normal Form (1NF) is a fundamental rule in database design that helps organise data efficiently. It ensures that a table is structured to eliminate duplicate and complex data, making it easier to manage and retrieve information.

Definition of 1NF

A table is said to be in First Normal Form (1NF) when:

• Each column contains only one value: no multiple values in a single cell.
• Each row is unique and can be identified using a primary key.
• There are no repeating groups or duplicate columns.

Simply put, 1NF ensures that data is stored clean, structured, and consistent.

Key Characteristics of 1NF

• Atomicity: Every cell in the table must have a single, indivisible value. For example, you should create separate rows or columns instead of storing multiple phone numbers in one column.
• Uniqueness: Each row in the table must be uniquely identifiable, typically using a primary key. This ensures that there are no duplicate records.
• No Repeating Groups: Data should not be stored in multiple columns when it can be organised in separate rows. This avoids unnecessary duplication and makes data retrieval easier.

Rules of First Normal Form

To organise data properly in a database, we follow certain rules. These rules ensure the data is clean, well-structured, and easy to work with. The First Normal Form (1NF) has three essential rules: removing duplicate columns, ensuring each piece of data is stored separately (atomicity), and using a unique identifier for each record. 

Let’s explore each rule in simple terms.

Eliminating Duplicate Columns

Imagine you are storing student exam scores in a table. If a student has taken multiple exams, you might create columns like Math_Score1, Math_Score2, Math_Score3, etc. This is a bad practice because adding more subjects would require new columns.

Instead, in 1NF, we store each piece of information in a structured way. We create separate rows for each exam, keeping only necessary columns. This makes data easier to manage and search.

Ensuring Atomicity (No Multi-Valued Attributes)

Each cell in a table should hold only one value, not a list. For example, if a customer has multiple phone numbers, storing them as “9876543210, 8765432109” in one column is incorrect. Instead, each phone number should be stored in a separate row. This makes searching and updating data much easier.

Presence of a Unique Identifier (Primary Key)

Every row in a table should have something that makes it unique. This could be a Student ID, Order Number, or Employee Code. A unique identifier (Primary Key) helps prevent duplicate records and ensures that each entry is distinct.

Examples of First Normal Form

Understanding the First Normal Form (1NF) is easier with examples. Let’s take a simple table that does not follow 1NF rules and then transform it into a proper 1NF-compliant format.

Example of a Table Not in 1NF

Consider a table storing student information, including the subjects they study:

This table is not in 1NF because the “Subjects” column contains multiple values (Math, Science, etc.) instead of a single value per row. In a well-structured database, each field should store only one piece of information per cell.

Converting the Table into 1NF-Compliant Form

To convert this table into First Normal Form (1NF), we must ensure that each field contains only one value. This means we must split the multi-valued “Subjects” column into separate rows:

Now, every cell contains a single value, making the table 1NF-compliant. If students study more than one subject, they appear in multiple rows, but every column now holds only one piece of data.

This approach makes the database cleaner, more structured, and easier to search. With this format, we can quickly find all students studying a particular subject without confusion.

Difference Between 1NF and Other Normal Forms

When organising data in a database, it is important to follow specific rules to avoid errors and inefficiencies. These rules are called normal forms, and they help structure data properly. First Normal Form (1NF) is the basic step of organising data, but databases often need further improvements using Second Normal Form (2NF) and beyond. 

Let’s explore how 1NF compares to Unnormalized Form (UNF) and how it differs from 2NF.

Comparison with Unnormalized Form (UNF)

Before applying 1NF, a table may be in Unnormalized Form (UNF). UNF means the data is stored without any structure or rules. A table in UNF often has:

• Repeating columns (e.g., storing multiple phone numbers in a single row).
  
• Multiple values in one cell (e.g., listing several product names in one field).

When we apply First Normal Form (1NF):

• Each cell contains only one value (no lists or sets).
• Every column stores only one type of data (e.g., a phone number column should not contain names).
• The table has a primary key, which uniquely identifies each row.

Converting a table from UNF to 1NF makes the data easier to manage, search, and update without confusion.

How 1NF Differs from Second Normal Form (2NF)

Once a table follows 1NF rules, it can still have problems like redundant data. That’s where Second Normal Form (2NF) comes in.

A table is in 2NF if:

•  It is already in 1NF.
• All data depends only on the primary key and not on part of it (if the key has multiple columns).

For example, if a sales table has Order ID and Product ID as a combined key but stores customer names in the same table, this breaks 2NF because the customer name only depends on Order ID, not the whole key. To fix this, we move such data to separate tables.

Applying 2NF reduces data duplication and improves database efficiency.

Advantages of First Normal Form

First Normal Form (1NF) makes databases more organised and efficient. It helps store data in a structured way, making it easier to manage and retrieve information. Here are some key benefits of using 1NF:

• Ensures Data Consistency and Accuracy: 1NF removes duplicate and inconsistent data, ensuring all information is stored correctly.
• Makes Data Easy to Find and Use: Since 1NF organises data neatly in tables, users can quickly search, sort, and retrieve the required information.
• Reduces Repeated Data: By eliminating redundancy, 1NF saves storage space and prevents confusion caused by duplicate records.

Limitations of First Normal Form

First Normal Form (1NF) helps organise data correctly but also has some drawbacks. When we follow 1NF rules, we may end up with more tables, making data handling a bit complex. Additionally, 1NF alone does not fully remove data repetition, so further steps are needed.

Here are two main limitations of 1NF:

• More Tables, More Space: Since we separate repeating data into different tables, the database may grow larger, requiring more storage.
• Not Fully Optimized: 1NF reduces some duplication, but some data may still repeat. We need further normalisation (like 2NF and 3NF) to fix this issue.

Closing Thoughts

Understanding the First Normal Form (1NF) is essential for designing structured, efficient, and scalable databases. By eliminating duplicate columns, ensuring atomicity, and using unique identifiers, 1NF improves data consistency and retrieval. 

However, further normalisation (2NF, 3NF) is required for complete optimisation. Mastering database normalisation enhances your SQL skills—an essential ability for data science professionals. 

To strengthen your SQL and data science expertise, explore the Pickl.AI Data Science Courses. These courses cover practical applications, real-world case studies, and hands-on projects, helping you build a strong data management and analytics foundation. Start learning today!

Frequently Asked Questions

What is the First Normal Form (1NF) in DBMS?

First Normal Form (1NF) ensures that database tables store data in a structured way. It removes duplicate columns, ensures atomicity, and assigns a primary key to each row. Following 1NF rules makes databases more organised, improving data retrieval, integrity, and efficiency.

What are some examples of the First Normal Form (1NF)?

A table with multiple values in a single cell, like storing multiple phone numbers in one column, violates 1NF. Converting it into 1NF involves separating values into distinct rows, ensuring each field holds a single value, and improving searchability and data consistency in DBMS.

Why is the First Normal Form (1NF) important?

First Normal Form (1NF) eliminates redundancy, organises data efficiently, and ensures easy retrieval. It prevents data anomalies and enhances accuracy by enforcing atomicity and uniqueness. Following 1NF is a fundamental step in designing scalable, well-structured databases, making data analysis and management more effective.