Database Normalization Demystified: How to Improve Data Integrity and Reduce Redundancy

What is Normalization?

Normalization is the process of organizing data in a database to reduce redundancy and improve data integrity. In a normalized database, data is structured across multiple tables, each focusing on a specific topic, and relationships between tables are established to maintain a logical data structure.

The main goals of normalization are to:

1. Eliminate Duplicate Data: Reduce data redundancy by storing each piece of information only once.
2. Ensure Data Consistency: Prevent inconsistencies by minimizing the chances of duplicated or outdated data entries.
3. Improve Data Integrity: Enforce rules to make sure data adheres to specified constraints and relationships, reducing errors.

1. First Normal Form (1NF): is the first step in the normalization process in database design. It ensures that each column in a table contains only atomic, indivisible values and eliminates any repeating groups or arrays. This form prevents any multivalued or composite attributes within a column and creates a more organized and structured data format.

Example: E-commerce Order System

Consider an e-commerce platform where order information is stored. Here is a table structure before applying First Normal Form (1NF):

In this table:

• The Products column contains multiple items in a single cell (e.g., "Laptop, Mouse, Keyboard").
• The Quantities column contains multiple quantities in a single cell corresponding to each product.
• This violates 1NF because each cell must contain a single, atomic value.

After Applying 1NF

To comply with 1NF, we need to break down the Products and Quantities columns into separate rows where each row has a single product and quantity per order.

2. Second Normal Form (2NF)

builds on First Normal Form (1NF) by eliminating partial dependencies in a relational database. For a table to be in 2NF, it must:

1. Be in First Normal Form (1NF): The table should have atomic values, with no repeating groups.
2. Have no Partial Dependencies: All non-key columns must be fully dependent on the entire primary key, not just part of it.

Example: E-commerce Orders with 2NF

Consider a case where we store order and product information in one table

In this table:

• The primary key is a composite key of OrderID and ProductID.
• Non-key columns, like CustomerName and Address, depend only on OrderID and not on ProductID.
• This dependency violates 2NF because CustomerName and Address are only partially dependent on OrderID and not on the full composite key (OrderID, ProductID).

After Applying 2NF

To achieve 2NF, we split the data into two tables: one for Orders (with customer information) and another for OrderDetails (with product information linked to each order).

Explanation

• In the Orders table, OrderID is the primary key, and all other columns (CustomerName, Address, OrderDate) depend on it.
• In the OrderDetails table, we use OrderDetailID as the primary key, and the non-key columns (ProductName, Quantity) depend on OrderID and Product

3. Third Normal Form (3NF) :

Removing transitive dependencies. For a table to be in 3NF, it must:

1. Be in Second Normal Form (2NF): All non-key columns should be fully dependent on the entire primary key.
2. Have no Transitive Dependencies: Non-key columns should depend only on the primary key and not on any other non-key column.

A transitive dependency occurs when a non-key attribute depends on another non-key attribute rather than directly on the primary key. By removing these dependencies, 3NF ensures that each non-key attribute is directly and only dependent on the primary key, making the data structure more efficient.

Example: E-commerce Platform in 3NF

let’s say we have an Orders table where we store some customer-related information that could cause a transitive dependency.

In this table:

• OrderID is the primary key.
• CustomerID is a foreign key.
• CustomerName and Address depend on CustomerID, not directly on OrderID.

This structure has a transitive dependency because CustomerName and Address depend on CustomerID, which itself depends on the primary key OrderID.

After Applying 3NF

To eliminate the transitive dependency, we can split this table into two separate tables: Orders and Customers.

Explanation

• In the Orders table, OrderID is the primary key, and CustomerID is stored as a foreign key.
• The Customers table has a unique CustomerID for each customer, and CustomerName and Address depend only on CustomerID.

This setup ensures that:

• Each table contains data that depends directly on its own primary key.
• There are no transitive dependencies in either table, meeting the criteria for 3NF.

4. Fourth Normal Form (4NF) :

Addresses multi-valued dependencies in a relational database. A table is in 4NF if it meets the following criteria:

1. Be in Third Normal Form (3NF): The table must already be in 3NF, meaning it has no transitive dependencies and all non-key attributes depend only on the primary key.
2. Have no Multi-valued Dependencies: A multi-valued dependency occurs when one attribute in a table uniquely determines another attribute, but both attributes are independent of each other. In simpler terms, if one attribute can have multiple values that don't affect the other attributes in the same table, it leads to a multi-valued dependency.

Example: E-commerce Platform in 4NF

Suppose we have a table that tracks products and their associated features.

In this table:

• ProductID is the primary key.
• A product can have multiple colors and sizes. The Color and Size attributes have a multi-valued dependency because the same product can have various colors and sizes independently.

After Applying 4NF

To eliminate the multi-valued dependencies, we can split this table into two separate tables: Products and ProductVariants.

Explanation

• In the Products table, ProductID and Color are stored together, capturing the unique colors for each product.
• The ProductVariants table captures the sizes independently, linked to ProductID.

This restructuring resolves the multi-valued dependency because:

• Each attribute is stored in a table where it only relates to the primary key, and there is no redundancy between the color and size attributes.

5. Fifth Normal Form (5NF) :

Also known as Project-Join Normal Form (PJNF), is a level of database normalization that deals primarily with the representation of data in such a way that it eliminates redundancy caused by the relationships between multiple entities. A table is in 5NF if:

1. It is in Fourth Normal Form (4NF): The table must already be in 4NF, meaning it has no multi-valued dependencies.
2. It cannot be decomposed into smaller tables without losing data: In other words, all the data must be represented in such a way that it cannot be divided further while maintaining the original information.

Example: E-commerce Platform in 5NF

Sells products to different customers, where some products can belong to multiple categories.

In this table:

• CustomerID identifies the customer.
• ProductID identifies the product.
• Category identifies the category of the product.

Here, the ProductID can belong to multiple Category entries, leading to redundancy.

After Applying 5NF

To achieve 5NF, we can break the table into three separate tables: Customers, Products, and Categories.

Explanation

• Customers Table: This table captures which products are associated with each customer.
• Products Table: This table connects products to their respective categories using a unique identifier (CategoryID).
• Categories Table: This table stores the distinct product categories.

By separating the data into these three tables, we eliminate redundancy and ensure that the information can be reconstructed through joins without any loss of data integrity.

Conclusion :

In conclusion, normalization is essential for creating efficient, reliable, and maintainable databases. While it may require more initial design effort and a deeper understanding of the data relationships, the long-term benefits in terms of data integrity, performance, and scalability make it a critical practice in database management. By implementing normalization, organizations can ensure their data is well-organized, accessible, and accurately reflects the relationships inherent in their operations.