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Introduction to Normal Forms



In database management systems (DBMS), the concept of normalization is employed to organize relational databases efficiently and to eliminate redundant data, ensure data dependency, and ensure data integrity. The process of normalization is divided into several stages, called "normal forms." Each normal form has a specific set of rules and criteria that a database schema must meet.

Here's a brief overview of the main normal forms:

1. First Normal Form (1NF)

  • Each table should have a primary key.
  • Atomic values: Each attribute (column) of a table should hold only a single value, meaning no repeating groups or arrays.
  • All entries in any column must be of the same kind.

Second Normal Form (2NF)

  • It meets all the requirements of 1NF.
  • It ensures that non-key attributes are fully functionally dependent on the primary key. In other words, if a table has a composite primary key, then every non-key attribute should be dependent on the full set of primary key attributes.

Third Normal Form (3NF)

  • It meets all the requirements of 2NF.
  • It ensures that the non-key columns are functionally dependent only on the primary key. This means there should be no transitive dependencies.

Boyce-Codd Normal Form (BCNF)

  • Meets all requirements of 3NF.
  • For any non-trivial functional dependency, X → Y, X should be a superkey. It's a more stringent version of 3NF.

Fourth Normal Form (4NF)

  • Meets all the requirements of BCNF.
  • There shouldn’t be any multi-valued dependency for a superkey. This deals with separating independent multiple relationships, ensuring that you cannot determine multiple sets of values in a table from a single key attribute.

Fifth Normal Form (5NF or Project-Join Normal Form - PJNF)

  • It deals with cases where certain projections of your data must be recreatable from other projections.

Sixth Normal Form (6NF)

  • Often considered when dealing with temporal databases (databases that have time-dependent data).
  • Deals with how data evolves over time and is less commonly discussed in most relational database design contexts.

Normalization often involves trade-offs. While higher normal forms eliminate redundancy and improve data integrity, they can also result in more complex relational schemas and sometimes require more joins, which can affect performance. As such, it's essential to understand the data and the specific application's requirements when deciding the level of normalization suitable for a particular situation. Sometimes, denormalization (intentionally introducing redundancy) is implemented to improve performance, especially in read-heavy databases.

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