Normalization

What is Normalization in SQL?
Normalization, in this context, is the process of organizing data within a database (relational database) to eliminate data anomalies, such as redundancy.

In simpler terms, it involves breaking down a large, complex table into smaller and simpler tables while maintaining data relationships.

Normalization is commonly used when dealing with large datasets.

Let’s take a brief look at some scenarios where normalization is often used.

Data integrity
Imagine a database that contains customer information. Without normalization, if a customer changes their age, we would need to update it in multiple places, which would increase the risk of inconsistencies. By normalizing the data, we can have separate tables linked by a unique identifier that will ensure that the data remains accurate and consistent.

Efficiency querying
Let’s consider a complex database with multiple related tables that stores redundant information. In this scenario, queries involving joins become more complicated and resource-intensive. Normalization will help simplify querying by breaking down data into smaller tables, with each table containing only relevant information, thereby reducing the need for complex joins.

Storage optimization
A major problem with redundant data is that it occupies unnecessary storage space. For instance, if we store the same product details in every order record, it leads to duplication. With normalization, you can eliminate redundancy by splitting data into separate tables.

Why is Normalization in SQL Important?
Normalization plays a crucial role in database design. Here are several reasons why it’s essential:

• Reduces redundancy: Redundancy is when the same information is stored multiple times, and a good way of avoiding this is by splitting data into smaller tables.
• Improves query performance: You can perform faster query execution on smaller tables that have undergone normalization.
• Minimizes update anomalies: With normalized tables, you can easily update data without affecting other records.
• Enhances data integrity: It ensures that data remains consistent and accurate.

What Causes the Need for Normalization?
If a table is not properly normalized and has data redundancy, it will not only take up extra data storage space but also make it difficult to handle and update the database.

There are several factors that drive the need for normalization, from data redundancy(as covered above) to difficulty managing relationships. Let’s get right into it:

• Insertion, deletion, and update anomalies: Any form of change in a table can lead to errors or inconsistencies in other tables if not handled carefully. These changes can either be adding new data to a database, updating the data, or deleting records, which can lead to unintended loss of data.
• Difficulty in managing relationships: It becomes more challenging to maintain complex relationships in an unnormalized structure.
• Other factors that drive the need for normalization are partial dependencies and transitive dependencies, in which partial dependencies can lead to data redundancy and update anomalies, and transitive dependencies can lead to data anomalies. We will be looking at how these dependencies can be dealt with to ensure database normalization in the coming sections.

Different Types of Database Normalization
So far, we have looked at what normalization in SQL is, why normalization in SQL is important, and what causes the need for normalization. Database normalization comes in different forms, each with increasing levels of data organization.

In this section, we will briefly discuss the different normalization levels and then explore them deeper in the next section.

First Normal Form (1NF)
This normalization level ensures that each column in your data contains only atomic values. Atomic values in this context means that each entry in a column is indivisible. It is like saying that each cell in a spreadsheet should hold just one piece of information. 1NF ensures atomicity of data, with each column cell containing only a single value and each column having unique names.

Second Normal Form (2NF)
Eliminates partial dependencies by ensuring that non-key attributes depend only on the primary key. What this means, in essence, is that there should be a direct relationship between each column and the primary key, and not between other columns.

Third Normal Form (3NF)
Removes transitive dependencies by ensuring that non-key attributes depend only on the primary key. This level of normalization builds on 2NF.

Boyce-Codd Normal Form (BCNF)
This is a more strict version of 3NF that addresses additional anomalies. At this normalization level, every determinant is a candidate key.

Fourth Normal Form (4NF)
This is a normalization level that builds on BCNF by dealing with multi-valued dependencies.

Fifth Normal Form (5NF)
5NF is the highest normalization level that addresses join dependencies. It is used in specific scenarios to further minimize redundancy by breaking a table into smaller tables.