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PART V
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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.
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32 15
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CHAPTER
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Optimization of data warehouse performance is a never-ending process. As technology vendors have improved the capabilities of computer hardware and software, warehouse developers have responded by storing and analyzing larger and more granular sets of data. In this ongoing quest for performance, the most powerful and effective tool at your disposal is the aggregate. Planned and integrated carefully, aggregates can have an extraordinary impact on the performance of your data warehouse. Their benefits can be realized without significant investment in specialized hardware and software, using tools that are already present in the data warehouse. Additional tools may help create and manage aggregates, but they are not required. Like a derived schema, an aggregate is a supplemental data structure that helps make things go faster. Derived schemas, which you learned about in the previous chapter, improve performance by restructuring data in a manner suited to a particular class of query. Aggregate schemas achieve a performance benefit by summarizing data. In this chapter, you will learn how to design and use aggregates. The principle of conformance, introduced in 5, will be put to use again, resulting in aggregates which provide query results that are consistent with the original schema. It also makes writing queries that leverage aggregates as simple as possible. Dimensional aggregation is also a very useful way to think about cube design in an environment that mixes star schemas and OLAP cubes. The benefits of aggregates are not free. Putting them to work requires selecting the correct aggregate to use for each query. It also requires that aggregates be populated with data and kept in sync with the base schema. The latter requirement can be particularly tricky when type 1 changes occur. Luckily, it is possible to minimize the impact of these requirements. The ideal aggregate would be invisible to users of the data warehouse, silently providing its benefit whenever possible. Although not mandatory, an aggregate navigation capability can help achieve this objective, and potentially provides a raft of additional benefits. The ideal aggregate would also be maintenance free, automatically generated and maintained without intervention by ETL developers. A variety of tools can be employed to fill this need as well.
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