My previous column described the three types of fact tables that are all you will ever need in your data warehouse (see December 2008 DM Review). The secret to this simple observation is adhering fanatically to the grain. A fact table records measurement events, and as long as we only record one kind of measurement event in a given fact table, we only need the three basic types: transaction grain, periodic snapshot grain and accumulating snapshot grain. This month, I'll describe basic ways to exploit these clean fact table designs in the front room and in the back room.
In my previous column, I showed that the most atomic grain of a measurement process is the most expressive. More dimensions can be attached to the atomic grain than can be attached to higher aggregated levels. The BI team should expose the atomic grain of a business process to the end users and application designers, allowing the data warehouse to choose aggregated levels of the data at run time, not design time. Thus, in our grocery store "beep" grain example, if the user's query asks for a category total, the database chooses a category-level aggregated fact table silently and behind the scenes at run time. In this way, aggregate tables are like indexes. Reports and queries should be designed without specific reference to the aggregate tables. If the user asks for a category total of specific products packaged in glass containers, then the database cannot use the simple category aggregate table and gracefully assembles the answer from the atomic fact table.
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