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What is First Normal Form (1NF)?

Learn how first normal form (1NF) can improve your database design by enforcing atomicity in your tables.
Jan 22, 2025  · 8 min read

If you're just starting with databases, you might not have encountered the chaos of messy, redundant data or the frustration of super-slow queries. But trust me, after five years of working across various startups and dealing with all kinds of database structures, I can tell you these issues pop up more often than you'd like.

Database normalization is one of the best ways to tackle these problems, and it all starts with first normal form (1NF). 1NF is a simple but powerful concept that helps create efficient, reliable, and maintainable databases by enforcing atomicity in your tables. It is also the foundation step for other normalization forms, like 2NF and 3NF.

In this article, I’ll walk you through everything you need to know about 1NF: what it is, how to apply it in practice, and why it matters. Whether you’re new to database design or just looking to level up your skills, this guide will help update your tables and lay a strong foundation for future normalization steps.

Historical Evolution of Database Normalization

When databases were first developed, their structures were far from what we consider “normalized” today. Early database systems often relied on flat files or hierarchical structures, which made managing and retrieving data cumbersome. These designs had significant limitations, including data redundancy, inconsistency, and inflexibility. The evolution of database normalization, starting with first normal form, addressed these challenges and revolutionized the way we store and manage data.

The origin of 1NF 

The concept of first normal form was introduced by Edgar F. Codd, widely regarded as the father of relational databases. In the 1970s, Codd proposed the relational model as an alternative to the hierarchical and network models that dominated early database systems. His motivation was clear: to create a systematic approach to organizing data that would reduce redundancy, eliminate anomalies, and make data manipulation more efficient.

At the heart of this model was 1NF, which set the foundational rule that every cell in a database table must hold a single, atomic value, and repeating groups should be eliminated. This principle addressed common problems in earlier systems, such as inconsistent updates and difficulties in querying nested or grouped data. Codd’s work on normalization not only solved immediate issues but also paved the way for more advanced database structures and operations.

From early databases to modern systems

The adoption of 1NF marked a turning point in database development. It formalized the process of organizing data into tables with clear relationships, laying the groundwork for SQL-based systems that dominate the database landscape today.

Before 1NF, systems like IBM’s Information Management System (IMS) relied on hierarchical structures, which required rigid tree-like designs. These systems struggled to adapt to changes in data or business needs. With the introduction of 1NF and the relational model, databases became more flexible, allowing for dynamic relationships between tables and greater scalability.

Hierarchical Database Model

Source: Airbyte

Modern relational database management systems (RDBMS) like MySQL, PostgreSQL, and Microsoft SQL Server owe their design principles to the foundational concepts of 1NF. These systems enable developers to work with well-structured data, which makes performing complex queries, updates, and integrations a lot easier.

Industry adoption

The practical benefits of 1NF – and database normalization in general – quickly gained traction across industries. In banking, for instance, 1NF helped manage customer records and transaction histories by ensuring that data was stored in a consistent and queryable format. Retailers used 1NF to track inventory, sales, and customer orders. Inventory management systems were another area where 1NF proved invaluable. By structuring data in atomic, non-repeating forms, manufacturers could maintain accurate records of components, stock levels, and supply chain movements.

Conditions for Achieving First Normal Form

Achieving 1NF requires meeting 4 key conditions:

  • Uniqueness of rows: Each row must be unique, with a distinct identifier. This is typically achieved by a primary key that ensures no duplicate rows.
  • No ordering assumptions: The order in which rows are stored or retrieved should not matter for the database design.
  • Atomicity: Each column must contain atomic (indivisible) values. This means there can be no sets, arrays, or lists of values in a single column.
  • No Duplicated Columns: There should be no repeated columns (i.e., no two columns storing the same type of information).

The first two conditions are generally enforced by design in most modern databases, so in this article we will focus on the last two: atomicity and unique columns.

Steps for Decomposing Tables to Achieve 1NF

There are simple steps to follow to decompose a table that violates 1NF.

Step 1: Identify non-atomic values and repeating groups

The first step is to locate columns that store multiple values in a single cell, or – in an effort to avoid doing just that – are repeated. These violations often appear as:

  • Lists or arrays (e.g., Multiple phone numbers in one column, separated by a comma).
  • Nested tables or structures embedded within a single cell.
  • Multiple columns representing the same type of data (e.g., three columns titled PhoneNumber1, PhoneNumber2, PhoneNumber3)

Step 2: Separate values into new rows

Next, we need to restructure the table so that each cell contains only one value, and so that each column represents one type of data. This often involves:

  • Duplicating rows as needed to ensure that every combination of data is preserved.
  • Creating new rows or even new tables to properly organize the data.

Step 3: Ensure consistency

After restructuring, check that your table meets the following 1NF requirements. If you are unsure, ask yourself “Can I sort, filter, or query the data easily without additional processing?”. “Does every column represent one type of attribute, and every row one unique record?” If you’ve answered yes to both questions, then the table is in 1NF.

Examples of First Normal Form in Practice

Now that we understand the steps to transform a table and achieve 1NF, let’s have a look at two practical first normal form examples.

Example 1: Customer orders table

Imagine a Customer Orders table that looks like this:

CustomerID Name Product1 Product2 OrderDate
1 Alice Apples Bananas 2025-01-10
2 Bob Oranges Grapes 2025-01-11

What’s wrong?

This table violates 1NF because it contains multiple columns, Product1 and Product2 that refer to the same type of data (products)l.

So how do we fix it?To achieve 1NF, split the data into separate rows so that each cell contains only one value:

CustomerID Name Product OrderDate
1 Alice Apples 2025-01-10
1 Alice Bananas 2025-01-10
2 Bob Oranges 2025-01-11
2 Bob Grapes 2025-01-11

Why does it matter?

In this first normal form example, we eliminated ambiguity, and made it simpler to query and analyze the data. Finding all customers who ordered bananas is now super easy!

Example 2: Student grades table

Let’s take a Student Grades table that looks like this:

StudentID Name Grades
101 Sarah A, B, C
102 Michael B, A, A

What’s wrong?

The Grades column violates 1NF because it stores multiple grades in a single cell, rather than using atomic values.

So how do we fix it?To comply with 1NF, we need to create separate rows for each grade:

StudentID Name Grade
101 Sarah A
101 Sarah B
101 Sarah C
102 Michael B
102 Michael A
102 Michael A

Why does it matter?

Breaking the grades into atomic values improves data organization and makes it easier to calculate averages, identify top-performing students, or analyze grade distributions.

If you want to get hands-on and create your own databases, have a look at our PostgresQL course. If you are a little more advanced, you could try this Introduction to Data Modeling in Snowflake, which covers ideas like entity-relationship and dimensional modeling.

Pros and Cons of First Normal Form

Let's look at the pluses and minuses:

Benefits of 1NF

Adopting 1NF offers some key benefits that make working with databases much easier and more efficient. 

By enforcing atomic values, it eliminates the chance of messy, concatenated data like "Apples, Bananas" in one row and "Apples;Bananas" in another. This uniformity makes the data cleaner and simpler to read, query, and update.

1NF also makes data manipulation a lot more straightforward. With atomic values, you can easily search, filter, and update the data without running into issues. For example, a query searching for "Apples" is much faster and more precise when each product is in its own row, rather than lumped together in a single column. It also reduced the risk of inconsistencies when performing updates, since changes are contained in specific rows.

Limitations of 1NF

1NF is not a perfect solution. Converting repeating columns into separate rows or breaking up multi-value cells into individual rows can lead to an increase in the length of the table. Since more rows can mean more data to sort through, this can have an impact on query performance.

It is important to note that 1NF is not an end in itself as it doesn’t tackle all forms of redundancy. Issues like partial dependencies or transitive dependencies are dealt with in higher normal forms like second normal form and third normal form. So, while 1NF is a solid foundation, it’s just the first step in database normalization and doesn’t fully optimize a database structure on its own.

Moving Beyond First Normal Form: Toward Second Normal Form (2NF)

The first database normalisation forms

The first database normalization forms. Source: Author

Once you’ve established your database is in first formal form, the next logical step is to move toward second normal form. 2NF builds on the foundation set by 1NF by addressing a key issue: partial dependencies.

In 2NF, the goal is to eliminate partial dependencies, which occur when a non-prime attribute (an attribute that isn’t part of the primary key) is dependent on only a portion of a composite primary key. This is particularly important in tables with composite keys – keys made up of multiple columns. This might sound a little abstract, but I wrote this guide to second normal form, which breaks the concept down and shows practical examples. You’ll get a good grasp of it in no time!

Conclusion

First normal form is the first step in database normalization, ensuring that each column contains atomic values and eliminating repeating columns.

As it sets the stage for more advanced normalization forms like 2NF and 3NF, you will most likely need to adopt 1NF if you plan on building efficient, scalable, and maintainable databases that can handle complex queries and large datasets with ease!

If you're ready to take your skills further, explore our Database Design course to deepen your understanding of normalization techniques and their practical applications. You can also validate your SQL and database management skills and demonstrate your expertise to potential employers with our SQL Associate Certification!


Marie Fayard's photo
Author
Marie Fayard

Senior Software Engineer, Technical Writer and Advisor with a background in physics. Committed to helping early-stage startups reach their potential and making complex concepts accessible to everyone.

1NF FAQs

Can I skip 1NF and move straight to higher normal forms like 2NF or 3NF?

No, 1NF is a foundational step in database normalization. Achieving 1NF is necessary before moving on to higher normal forms like 2NF or 3NF, as they build upon the structure established by 1NF.

Is 1NF the same for all types of databases?

While relational databases require 1NF, NoSQL databases (like MongoDB) may not follow this rule. In NoSQL systems, data is often stored in ways that don't strictly adhere to the principles of normalization.

What tools can I use to check if my table is in 1NF?

While there isn’t a specific tool that checks 1NF automatically, you can use database design tools like MySQL Workbench or dbdiagram.io to visualize your schema. You can also manually inspect your data for atomicity and ensure there are no repeating groups or arrays.

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