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  2. Star schema - Wikipedia

    en.wikipedia.org/wiki/Star_schema

    The star schema is an important special case of the snowflake schema, and is more effective for handling simpler queries. [2] The star schema gets its name from the physical model's [3] resemblance to a star shape with a fact table at its center and the dimension tables surrounding it representing the star's points.

  3. Database model - Wikipedia

    en.wikipedia.org/wiki/Database_model

    Others arrived in much the same place by adding relational features to pre-relational systems. Paradoxically, this allows products that are historically pre-relational, such as PICK and MUMPS, to make a plausible claim to be post-relational. The resource space model (RSM) is a non-relational data model based on multi-dimensional classification. [5]

  4. Dimensional modeling - Wikipedia

    en.wikipedia.org/wiki/Dimensional_modeling

    The dimensional model is built on a star-like schema or snowflake schema, with dimensions surrounding the fact table. [3] [4] To build the schema, the following design model is used: Choose the business process; Declare the grain; Identify the dimensions; Identify the fact; Choose the business process

  5. Data warehouse - Wikipedia

    en.wikipedia.org/wiki/Data_warehouse

    The two most important approaches to store data in a warehouse are dimensional and normalized. The dimensional approach uses a star schema as proposed by Ralph Kimball. The normalized approach, also called the third normal form (3NF) is an entity-relational normalized model proposed by Bill Inmon. [21]

  6. Database schema - Wikipedia

    en.wikipedia.org/wiki/Database_schema

    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 ).

  7. Dimension (data warehouse) - Wikipedia

    en.wikipedia.org/wiki/Dimension_(data_warehouse)

    A dimension table in an OLAP cube with a star schema. A dimension is a structure that categorizes facts and measures in order to enable users to answer business questions. Commonly used dimensions are people, products, place and time. [1] [2] (Note: People and time sometimes are not modeled as dimensions.)

  8. Data mart - Wikipedia

    en.wikipedia.org/wiki/Data_mart

    Data Warehouse and Data Mart overview, with Data Marts shown in the top right.. A data mart is a structure/access pattern specific to data warehouse environments. The data mart is a subset of the data warehouse that focuses on a specific business line, department, subject area, or team. [1]

  9. Online analytical processing - Wikipedia

    en.wikipedia.org/wiki/Online_analytical_processing

    The cube metadata is typically created from a star schema or snowflake schema or fact constellation of tables in a relational database. Measures are derived from the records in the fact table and dimensions are derived from the dimension tables. Each measure can be thought of as having a set of labels, or meta-data associated with it.