A measure represents a column that contains quantifiable data, usually numeric, that can be aggregated. A measure is generally mapped to a column in a fact table.
Attribute columns from dimension tables can be used to define measures, but such measures are typically semiadditive or nonadditive in terms of their aggregation behavior.
You can also define a measure as a calculated member by using a Multidimensional Expressions (MDX) to provide a calculated value for a measure based on other measures in the cube.
Measure Groups
In a cube, measures are grouped by their underlying fact tables into measure groups. Measure groups are used to associate dimensions with measures.
The fact table contains two basic types of columns: attribute columns and measure columns. Attribute columns are used to create foreign key relationships to dimension tables, so that the quantifiable data in the measure columns can be organized by the data contained in the dimension tables.Attribute columns are also used to define the granularity of a fact table and its measure group.
Granularity
Granularity refers to the level of detail supported by a fact table.
The granularity of a measure group can never be set finer than the lowest level of the dimension from which the measure group is viewed, but the granularity can be made coarser by using additional attributes.
Aggregate Functions
When a dimension is used to organize measures in a measure group, the measure is summarized along the hierarchies contained in that dimension. The summation behavior depends on the aggregate function specified for the measure.
