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Relatively little is written about derived schemas in dimensional modeling, other than snapshots and accumulating snapshots. Perhaps this is so because derived schemas are easy to intuit when the situation calls for them. In situations that call for derived schemas, Kimball and Ross refer to the original dimension structure as a first-level data mart, and the derived structure as a consolidated data mart. This terminology has been avoided here, since this book reserves the term data mart for a subject area, rather than a single star. Recognizing what Kimball and Ross mean by these terms, however, will help you interpret some of the following references: Kimball and Ross allude to the use of derived tables for profitability by precomputing the results of drilling across several stars representing costs and revenue. This is described in 3 of The Data Warehouse Toolkit, Second Edition (Wiley, 2002). That same chapter suggests that some consolidated data marts may represent a simple union of first-level data marts. As you read it, think of deriving a core fact table from custom ones, or deriving a whole from slices. Kimball and Ross describe a merged fact table that can be used to study budget variances in 7 of The Data Warehouse Toolkit. This is similar to the goal versus performance example from this chapter. Kimball et al. describe a merged fact table used to compare forecast to actual data in 6 of The Data Warehouse Lifecycle Toolkit, Second Edition (Wiley, 2008) by Ralph Kimball, Margy Ross, Warren Thornthwaite, Joy Mundy, and Bob Becker. Like any other dimensional schema, a derived table may serve as the basis for a presummarized aggregate. In 9 of Mastering Data Warehouse Aggregates (Wiley, 2006), I discuss the construction of aggregates to supplement merged, pivoted, and sliced fact tables. barcode data entry excel Barcode Add in for Word and Excel - Free download and software ...
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