Database Normalization for Beginners

A Comprehensive Guide on Database Normalization for Beginners:

Database normalization is a fundamental concept in the realm of databases, particularly relational databases. Whether you’re a beginner or looking to refresh your knowledge, this comprehensive guide will introduce you to the concept of database normalization, explain the rules at every stage, and cover relevant terms that you’ll encounter along the way. So let’s dive in and discover how you can effectively organize your data!

What is Database Normalization?

Database normalization is a process that helps structure and organize data within a relational database. It aims to minimize data redundancy, reduce anomalies, and improve data integrity. By breaking down a database into multiple tables and establishing relationships between them, normalization provides a solid foundation for efficient data management.

Why Normalize a Database?

Normalization comes with several benefits:

• Efficiency: A normalized database is more efficient in terms of storage and query performance.
• Consistency: It ensures that each piece of data is stored only once, which maintains consistency across the database.
• Flexibility: Changes in data requirements or business rules are easier to accommodate in a normalized database.

Definition of Key Terms

Before we delve into the rules of normalization, let’s familiarize ourselves with some key terms:

• Primary Key: A column or a set of columns that uniquely identifies each row in a table.
• Foreign Key: A foreign key is a field in one table that refers to the primary key in another table, establishing a relationship between the two tables.
• Composite Key: A composite key is a key that consists of two or more columns. It is used when a single column doesn’t uniquely identify a record, but the combination of multiple columns does.
• Partial Dependency: A partial dependency occurs when a non-key attribute depends on only a portion of the primary key, violating the rules of normalization.
• Transitive Dependency: Transitive dependency happens when a non-key attribute depends on another non-key attribute, rather than directly on the primary key.

The Normal Forms

Normalization is typically carried out in stages, each stage corresponding to a “normal form.” There are several normal forms, but the most commonly used are the first three:

1. First Normal Form (1NF): This form requires that each column of a table be atomic, meaning it cannot contain multiple values. It also requires a unique identifier for each row, known as a primary key.
2. Second Normal Form (2NF): A table is in 2NF if it is in 1NF and all non-key attributes are fully functionally dependent on the primary key.
3. Third Normal Form (3NF): A table is in 3NF if it is in 2NF and all its attributes are not only fully functionally dependent on the primary key but also non-transitively dependent.

Database Normalization Rules

Rules for First Normal Form (1NF)

1. Atomicity:

Each column must contain atomic values, meaning the values are indivisible. This ensures that each field contains only a single piece of information. There should be no sets of values or lists within a single cell. If a column contains multiple values, it violates 1NF. To adhere to 1NF, you need to split such columns into separate columns or create additional tables to represent the relationship

2. No Repeating Groups:

A table should not have repeating columns or groups of data. This means that data typically stored in array-like structures should be separated into different rows or tables.

3. Unique Rows:

Each record within the table must be unique. This is often enforced by the use of a primary key.

4. Unique Column Names

Each column in a table must have a unique name to avoid confusion and ambiguity. This ensures that each attribute is uniquely identifiable within the table structure.

Rules for Second Normal Form (2NF)

1. Database Must Be in First Normal Form (1NF):

The table must already be in 1NF, which means it has no repeating groups, each field contains only atomic values, and each record is unique.

2. No Partial Dependency:

Ensure that each non-key attribute (other columns besides the primary key) in your table depends on the entire primary key, not just part of it. In other words, each non-key attribute should be functionally dependent on the entire primary key

Rules for Third Normal Form (3NF)

1. Database Must Be in Second Normal Form (2NF):

The table must already be in 2NF, which means it is in 1NF and all non-key attributes are fully functionally dependent on the primary key.

2. No Transitive Dependency:

In 3NF, there should be no transitive dependency for non-prime attributes. This means a non-key attribute should not depend on another non-key attribute.

3. Dependency on Primary Key:

All attributes in the table must be directly dependent on the primary key. This ensures that the non-key attributes are not dependent on other non-key attributes, which could lead to indirect relationships that complicate the data structure.

How to Normalize a Database?

To normalize a database, you start with an unnormalized table and apply the rules for each normal form sequentially. Here’s a simplified example:

Unnormalized Table:

Imagine we have the following table that records student enrollments and the instructors for each course:

This table is unnormalized because the Courses Enrolled and Instructors columns contain multiple values, and there is redundant data.

Database Anomalies

Insertion Anomaly

An insertion anomaly occurs when certain attributes cannot be inserted into the database without the presence of other attributes.

Example: Suppose we want to add a new course, “Art,” which doesn’t have any students enrolled yet. In this unnormalized table, we cannot add “Art” without assigning a student to it, which is not practical.

Update Anomaly

An update anomaly is when the same information is duplicated across multiple rows, and updates are not properly propagated to all rows.

Example: If Mr. Smith changes his phone number, we would have to update multiple rows (for each course he teaches). If we forget to update all rows, the database will have inconsistent information about Mr. Smith’s contact details.

Deletion Anomaly

A deletion anomaly occurs when deleting a row causes loss of other valuable data.

Example: If Charlie drops out and we delete his row, we lose the information that Mr. Smith is teaching Math. This is because the course information is tied to the student information.

Normalization to Prevent Anomalies

To prevent these anomalies, we normalize the database:

First Normal Form (1NF)

Step: Eliminate repeating groups in individual tables.

Changes Made:

• Create a new row for each value that was in a list.
• Ensure each column has atomic values.

1NF Table:

Second Normal Form (2NF)

Step: Ensure that all non-key attributes are fully functionally dependent on the primary key.

Changes Made:

• Remove partial dependencies by separating data into different tables.

2NF Tables:

Students Table:

Courses Table:

Enrollments Table:

Third Normal Form (3NF)

Step: Remove transitive dependencies.

Changes Made:

• If an attribute depends on another non-primary key attribute, move it to a separate table.

Let’s say we find out that instructors belong to departments, which is a transitive dependency.

3NF Tables:

Students Table (Unchanged):

Courses Table:

Enrollments Table (Unchanged):

Instructors Table:

Course Instructors Table:

In this 3NF example, we’ve removed the Instructor column from the Courses table and created a new Instructors table that relates instructors to their respective departments. We also created a Course Instructors table to maintain the relationship between courses and instructors. This ensures that all attributes in each table are only dependent on the primary key, thus eliminating transitive dependencies.

Through these steps, we’ve normalized our data from an unstructured table into a set of well-structured tables that reduce redundancy and improve data integrity. Each normal form builds upon the previous one, further refining the database structure.

Conclusion

Database normalization is a fundamental concept in designing efficient and well-structured databases. By adhering to the rules of normalization, you can eliminate redundancies, reduce anomalies, and ensure data integrity. Understanding terms like foreign keys, composite keys and dependencies such as partial and transitive dependencies is crucial for effective normalization.