What are the different levels of normalization?

Database normalization is organized into levels called normal forms (1NF to 5NF), each addressing specific data redundancy and integrity issues. The first three normal forms (1NF, 2NF, 3NF) are the most widely used, while Boyce-Codd Normal Form (BCNF), 4NF, and 5NF address more complex scenarios. Each level builds on the previous one, enforcing stricter rules to eliminate anomalies and ensure efficient data organization. For most applications, achieving 3NF or BCNF is sufficient, though higher forms are useful in specialized cases involving multi-valued or join dependencies.

First Normal Form (1NF) requires that tables have atomic values (no repeating groups or arrays) and a unique primary key. For example, a table storing customer orders with a column like Products containing comma-separated values (e.g., “Book, Pen, Laptop”) violates 1NF. To fix this, split the data into individual rows, each listing one product per order. Second Normal Form (2NF) adds that non-key attributes must depend on the entire primary key, not just part of it. Consider an OrderDetails table with a composite key of OrderID and ProductID. If the ProductName column depends only on ProductID (not the full key), it creates redundancy. To resolve this, move ProductName to a separate Products table linked by ProductID.

Third Normal Form (3NF) eliminates transitive dependencies, where a non-key attribute depends on another non-key attribute. For instance, a Customers table with CustomerID, ZipCode, and City might have City determined by ZipCode (not directly by CustomerID). To normalize, create a ZIPCodes table linking ZipCode to City. BCNF tightens 3NF by requiring that every determinant (attribute influencing others) must be a candidate key. For example, if a table tracks StudentID, Course, and Instructor, and Instructor determines Course, BCNF mandates splitting the table to avoid dependency on a non-key attribute. Higher forms like 4NF and 5NF address multi-valued dependencies (e.g., splitting tables where independent attributes have multiple values) and complex join scenarios, but these are rarely needed outside niche use cases.