Database Normalisation Questions Medium
The process of normalizing a database involves organizing and structuring the data in a way that reduces redundancy and improves data integrity. It is a systematic approach that follows a set of rules and guidelines to ensure that the database is efficient, flexible, and free from anomalies.
The process of normalizing a database typically involves the following steps:
1. Identify the entities: Identify the main entities or objects that the database will store information about. These entities can be anything from customers, products, or orders.
2. Define the attributes: Determine the specific characteristics or properties of each entity. For example, a customer entity may have attributes such as name, address, and contact information.
3. Establish relationships: Determine the relationships between different entities. This involves identifying how entities are related to each other, such as a customer placing an order or a product being part of a specific category.
4. Apply normalization rules: Apply a set of normalization rules to eliminate redundancy and ensure data integrity. The most commonly used normalization rules are based on the normal forms, such as First Normal Form (1NF), Second Normal Form (2NF), and Third Normal Form (3NF).
5. Normalize the data: Break down the data into smaller, more manageable tables based on the normalization rules. This involves splitting the data into separate tables to eliminate repeating groups and dependencies.
6. Define primary and foreign keys: Identify the primary key for each table, which uniquely identifies each record. Establish foreign keys to establish relationships between tables.
7. Review and refine: Review the normalized database design and refine it if necessary. This may involve making adjustments to the table structure, relationships, or attributes based on the specific requirements of the database.
By following the process of normalization, a database can be optimized for efficient storage, retrieval, and manipulation of data. It helps to minimize data redundancy, improve data consistency, and ensure data integrity, ultimately leading to a more robust and reliable database system.