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Snowflake schema used by example query. The example schema shown to the right is a snowflaked version of the star schema example provided in the star schema article. The following example query is the snowflake schema equivalent of the star schema example code which returns the total number of television units sold by brand and by country for 1997.
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.
Dan Linstedt, the creator of the method, describes the resulting database as follows: "The Data Vault Model is a detail oriented, historical tracking and uniquely linked set of normalized tables that support one or more functional areas of business. It is a hybrid approach encompassing the best of breed between 3rd normal form (3NF) and star ...
Example of a star schema; the central table is the fact table. In data warehousing, a fact table consists of the measurements, metrics or facts of a business process. It is located at the center of a star schema or a snowflake schema surrounded by dimension tables. Where multiple fact tables are used, these are arranged as a fact constellation ...
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
Examples of denormalization techniques include: "Storing" the count of the "many" elements in a one-to-many relationship as an attribute of the "one" relation; Adding attributes to a relation from another relation with which it will be joined; Star schemas, which are also known as fact-dimension models and have been extended to snowflake schemas
These schemas are implemented for complex data warehouses. [1] The fact constellation is a measure of online analytical processing and can be seen as an extension of the star schema. A fact constellation schema has multiple fact tables. It is a widely used schema and more complex than star schemas and snowflake schemas.
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.