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Usually dimension tables do not reference other dimensions via foreign keys. When this happens, the referenced dimension is called an outrigger dimension. Outrigger dimensions should be considered a data warehouse anti-pattern: it is considered a better practice to use some fact tables that relate the two dimensions. [9]
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 ...
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 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 ...
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. This step is closely related to the business users of the system, since this is where they get access to data stored in the data warehouse ...
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.
Small data marts can shop for data from the consolidated warehouse and use the filtered, specific data for the fact tables and dimensions required. The data warehouse provides a single source of information from which the data marts can read, providing a wide range of business information.
When facts are aggregated, it is either done by eliminating dimensionality or by associating the facts with a rolled up dimension. Rolled up dimensions should be shrunken versions of the dimensions associated with the granular base facts. This way, the aggregated dimension tables should conform to the base dimension tables. [2]