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It is the second of the two principles of database design, which seek to prevent databases from being too complicated or redundant, the first principle being the principle of full normalization . Simply put, it says that no two relations in a relational database should be defined in such a way that they can represent the same facts.
Database design is the organization of data according to a database model.The designer determines what data must be stored and how the data elements interrelate. With this information, they can begin to fit the data to the database model. [1]
Codd's twelve rules [1] are a set of thirteen rules (numbered zero to twelve) proposed by Edgar F. Codd, a pioneer of the relational model for databases, designed to define what is required from a database management system in order for it to be considered relational, i.e., a relational database management system (RDBMS).
The database design documented in these schemas is converted through a Data Definition Language, which can then be used to generate a database. A fully attributed data model contains detailed attributes (descriptions) for every entity within it. The term "database design" can describe many different parts of the design of an overall database ...
Normalization is a database design technique, which is used to design a relational database table up to higher normal form. [9] The process is progressive, and a higher level of database normalization cannot be achieved unless the previous levels have been satisfied.
Database theory encapsulates a broad range of topics related to the study and research of the theoretical realm of databases and database management systems.. Theoretical aspects of data management include, among other areas, the foundations of query languages, computational complexity and expressive power of queries, finite model theory, database design theory, dependency theory, foundations ...
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"Don't repeat yourself" (DRY), also known as "duplication is evil", is a principle of software development aimed at reducing repetition of information which is likely to change, replacing it with abstractions that are less likely to change, or using data normalization which avoids redundancy in the first place.