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Normal forms are database normalization levels which determine the "goodness" of a table. Generally, the third normal form is considered to be a "good" standard for a relational database. [citation needed] Normalization aims to free the database from update, insertion and deletion anomalies.
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.
Data Warehouse and Data mart overview, with Data Marts shown in the top right.. In computing, a data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis and is a core component of business intelligence. [1]
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 .
The third normal form (3NF) is a normal form used in database normalization. 3NF was originally defined by E. F. Codd in 1971. [2] Codd's definition states that a table is in 3NF if and only if both of the following conditions hold: The relation R (table) is in second normal form (2NF).
Domain-key normal form (DK/NF or DKNF) is a normal form used in database normalization which requires that the database contains no constraints other than domain constraints and key constraints. A domain constraint specifies the permissible values for a given attribute, while a key constraint specifies the attributes that uniquely identify a ...
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Anchor modeling was created in order to take advantage of the benefits from a high degree of normalization while avoiding its drawbacks which higher normal forms have with regards to human readability. Advantages such as being able to non-destructively evolve the model, avoid null values, and keep the information free from redundancies are gained.