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Concept
Star schema
Summary
In computing, the star schema is the simplest style of data mart schema and is the approach most widely used to develop data warehouses and dimensional data marts. The star schema consists of one or more fact tables referencing any number of dimension tables. The star schema is an important special case of the snowflake schema, and is more effective for handling simpler queries.
The star schema gets its name from the physical model's resemblance to a star shape with a fact table at its center and the dimension tables surrounding it representing the star's points.
The star schema separates business process data into facts, which hold the measurable, quantitative data about a business, and dimensions which are descriptive attributes related to fact data. Examples of fact data include sales price, sale quantity, and time, distance, speed and weight measurements. Related dimension attribute examples include product models, product colors, product sizes, geographic locations, and salesperson names.
A star schema that has many dimensions is sometimes called a centipede schema. Having dimensions of only a few attributes, while simpler to maintain, results in queries with many table joins and makes the star schema less easy to use.
Fact tables record measurements or metrics for a specific event.
Fact tables generally consist of numeric values, and foreign keys to dimensional data where descriptive information is kept.
Fact tables are designed to a low level of uniform detail (referred to as "granularity" or "grain"), meaning facts can record events at a very atomic level. This can result in the accumulation of a large number of records in a fact table over time. Fact tables are defined as one of three types:
Transaction fact tables record facts about a specific event (e.g., sales events)
Snapshot fact tables record facts at a given point in time (e.g., account details at month end)
Accumulating snapshot tables record aggregate facts at a given point in time (e.g.
Official source
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