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The fact table contains business facts (or measures), and foreign keys which refer to candidate keys (normally primary keys) in the dimension tables. Contrary to fact tables, dimension tables contain descriptive attributes (or fields) that are typically textual fields (or discrete numbers that behave like text).
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 ...
Fact_Sales is the fact table and there are three dimension tables Dim_Date, Dim_Store and Dim_Product. Each dimension table has a primary key on its Id column, relating to one of the columns (viewed as rows in the example schema) of the Fact_Sales table's three-column (compound) primary key ( Date_Id , Store_Id , Product_Id ).
These dimensions are where all the data is stored. For example, the date dimension could contain data such as year, month and weekday. Identify the facts. After defining the dimensions, the next step in the process is to make keys for the fact table. This step is to identify the numeric facts that will populate each fact table row.
A fact is represented by a box that displays the fact name along with the measure names. Small circles represent the dimensions, which are linked to the fact by straight lines (see Figure 1). A dimensional attribute is a property, with a finite domain, of a dimension. Like dimensions, a dimensional attribute is represented by a circle.
The snowflake schema is represented by centralized fact tables which are connected to multiple dimensions. "Snowflaking" is a method of normalizing the dimension tables in a star schema. When it is completely normalized along all the dimension tables, the resultant structure resembles a snowflake with the fact table in the middle. The principle ...
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According to Ralph Kimball, [1] in a data warehouse, a degenerate dimension is a dimension key (primary key for a dimension table) in the fact table that does not have its own dimension table, because all the interesting attributes have been placed in analytic dimensions. The term "degenerate dimension" was originated by Ralph Kimball.