Normalization in SQL databases is the process of organizing data to reduce redundancy and improve integrity. It involves structuring tables and relationships to ensure each piece of data is stored logically and efficiently. The primary goals are to eliminate duplicate data, minimize update anomalies, and simplify queries. For example, instead of storing a customer’s name and address in every order record, normalization would split this into separate tables (e.g., Customers and Orders) linked by a unique identifier like customer_id. This avoids inconsistencies when customer details change, as updates only need to occur in one place.
Normalization is achieved through a series of rules called “normal forms,” which define how data should be organized. The first three normal forms (1NF, 2NF, 3NF) are the most commonly applied. 1NF requires atomic values (no repeating groups or arrays) in each column. For instance, a column storing multiple phone numbers as comma-separated values violates 1NF; instead, each number should be its own row. 2NF builds on 1NF by removing partial dependencies—data that depends on only part of a composite primary key. If an OrderDetails table uses order_id and product_id as a composite key, columns like product_name should not depend solely on product_id (they belong in a Products table). 3NF removes transitive dependencies—data that depends on non-key columns. For example, if a Customers table includes city and zip_code, and city can be determined from zip_code, city should be moved to a ZipCodes table linked by zip_code.
The benefits of normalization include reduced storage usage, improved data consistency, and easier maintenance. However, over-normalization can lead to performance issues due to excessive joins in queries. For example, retrieving a customer’s order history might require joining Customers, Orders, OrderDetails, and Products tables. In read-heavy applications, some denormalization (e.g., caching frequently accessed data in a single table) might be necessary for speed. Developers should balance normalization with practical performance needs, tailoring the structure to the application’s specific requirements.