Database design refers to the process of creating a structured representation of the data that will be stored in a database. The goal of database design is to ensure that the database is efficient, well-organized, and able to meet the needs of the users.
Normalization, on the other hand, is a technique used in database design to eliminate data redundancy and improve data integrity. It involves breaking down larger tables into smaller, more manageable ones and establishing relationships between them.
The process of normalization involves several steps, known as normal forms. These include the First Normal Form (1NF), Second Normal Form (2NF), Third Normal Form (3NF), and so on. Each normal form builds upon the previous one, adding additional constraints and requirements for data organization.
In general, normalization involves the following steps:
1. Identify the entity types: Determine the entities (objects, people, places, etc.) that will be represented in the database.
2. Define the attributes: Identify the characteristics or properties that are relevant to each entity.
3. Identify the relationships: Determine how the entities are related to each other. This could be a one-to-one, one-to-many, or many-to-many relationship.
4. Normalize the data: Apply the normalization rules to eliminate data redundancy and improve data integrity. This involves dividing larger tables into smaller ones, establishing relationships between them, and ensuring that each table contains a single theme or subject.
5. Define the primary key: Determine the attribute(s) or combination of attributes that uniquely identify each row in a table. This is usually done by selecting a primary key from the set of attributes.
6. Establish the foreign key: Determine the attributes in one table that refer to the primary key of another table. This establishes the relationship between the two tables.
By following these steps, the database designer can create a well-structured and normalized database that is optimized for efficient storage and retrieval of data. This can result in improved performance, reduced data redundancy, and increased data integrity.