In data warehousing, a fact table consists of the measurements, metrics or facts of a business process. It is often located at the centre of a star schema, surrounded by dimension tables.
Fact tables provide the (usually) additive values that act as independent variables by which dimensional attributes are analyzed. Fact tables are often defined by their grain. The grain of a fact table represents the most atomic level by which the facts may be defined. The grain of a SALES fact table might be stated as “Sales volume by Day by Product by Store”. Each record in this fact table is therefore uniquely defined by a day, product and store. Other dimensions might be members of this fact table (such as location/region) but these add nothing to the uniqueness of the fact records. These “affiliate dimensions” allow for additional slices of the independent facts but generally provide insights at a higher level of aggregation (a region contains many stores).
* Additive - Measures that can be added across all dimensions.
* Non Additive - Measures that cannot be added across all dimensions.
* Semi Additive – Measures that can be added across few dimensions and not with others.