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The dimensional model is a specialized adaptation of the relational model used to represent data in data warehouses in a way that data can be easily summarized using online analytical processing, or OLAP queries. In the dimensional model, a database schema consists of a single large table of facts that are described using dimensions and measures.
Dimensional models are more denormalized and optimized for data querying, while normalized models seek to eliminate data redundancies and are optimized for transaction loading and updating. The predictable framework of a dimensional model allows the database to make strong assumptions about the data which may have a positive impact on performance.
A relational database (RDB [1]) is a database based on the relational model of data, as proposed by E. F. Codd in 1970. [ 2 ] A database management system used to maintain relational databases is a relational database management system ( RDBMS ).
In computing, the star schema or star model is the simplest style of data mart schema and is the approach most widely used to develop data warehouses and dimensional data marts. [1] The star schema consists of one or more fact tables referencing any number of dimension tables .
The dimension is a data set composed of individual, non-overlapping data elements. The primary functions of dimensions are threefold: to provide filtering, grouping and labelling. These functions are often described as "slice and dice". A common data warehouse example involves sales as the measure, with customer and product as dimensions.
A data type in a relational database might be the set of integers, the set of character strings, the set of dates, etc. The relational model does not dictate what types are to be supported. Attributes are commonly represented as columns, tuples as rows, and relations as tables. A table is specified as a list of column definitions, each of which ...
Data modeling techniques and methodologies are used to model data in a standard, consistent, predictable manner in order to manage it as a resource. The use of data modeling standards is strongly recommended for all projects requiring a standard means of defining and analyzing data within an organization, e.g., using data modeling:
Overview of a data-modeling context: Data model is based on Data, Data relationship, Data semantic and Data constraint. A data model provides the details of information to be stored, and is of primary use when the final product is the generation of computer software code for an application or the preparation of a functional specification to aid a computer software make-or-buy decision.