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Database normalization is the process of structuring a relational database accordance with a series of so-called normal forms in order to reduce data redundancy and improve data integrity. It was first proposed by British computer scientist Edgar F. Codd as part of his relational model .
This was the first time the notion of a relational database was published. All work after this, including the Boyce–Codd normal form method was based on this relational model. The Boyce–Codd normal form was first described by Ian Heath in 1971, and has also been called Heath normal form by Chris Date .
The purpose of this normalization is to increase flexibility and data independence, and to simplify the data language. It also opens the door to further normalization, which eliminates redundancy and anomalies. Most relational database management systems do not support nested records, so tables are in first normal form by default.
In the object-oriented application programming paradigm, which is related to database structure design, UML class diagrams may be used for object modeling. In that case, object relationships are modeled using UML associations, and multiplicity is used on those associations to denote cardinality. Here are some examples: [5]
Normalization consists of normal forms that are 1NF, 2NF, 3NF, Boyce-Codd NF (3.5NF), 4NF, 5NF and 6NF. Document databases take a different approach. A document that is stored in such a database, typically would contain more than one normalized data unit and often the relationships between the units as well.
The database schema is the structure of a database described in a formal language supported typically by a relational database management system (RDBMS). The term " schema " refers to the organization of data as a blueprint of how the database is constructed (divided into database tables in the case of relational databases ).
Denormalization is a strategy used on a previously-normalized database to increase performance. In computing, denormalization is the process of trying to improve the read performance of a database, at the expense of losing some write performance, by adding redundant copies of data or by grouping data.
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