How do you normalize data models?

1 What is data normalization?

Data normalization is a technique that helps you design efficient and consistent databases, eliminating or minimizing the problems of data anomalies, data redundancy, and data dependency. Data anomalies occur when you insert, update, or delete data in a table that is not normalized. Data redundancy is the duplication of data across multiple tables or within the same table, which wastes storage space and increases the risk of data inconsistency. Data dependency is when the value of one attribute depends on the value of another attribute in the same table or a different table, making the data model complex and difficult to maintain.

2 What are normal forms?

Normal forms are a set of rules or criteria that you can apply to your data model to check and improve its level of normalization. There are several normal forms, but the most commonly used ones are the first, second, and third normal forms (1NF, 2NF, and 3NF). Each normal form builds on the previous one, so you need to satisfy the lower normal forms before moving to the higher ones.

3 How to apply the first normal form (1NF)?

The first normal form (1NF) requires that every table in your data model has a primary key, which is a unique identifier for each row. It also requires that every attribute in each table is atomic, meaning that it cannot be further divided into smaller parts. For example, a name attribute should not contain both first name and last name, but rather be split into two separate attributes. Additionally, 1NF does not allow any repeating groups or arrays of values in a single attribute. For example, a hobbies attribute should not contain a list of hobbies, but rather be moved to a separate table with a foreign key referencing the primary key of the original table.