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Performance is a guiding principle of dimensional design, as you learned in 1. It is also one of the foundational principles of data warehousing in general. By restructuring data at load time, rather than query time, answering analytic questions about business processes is faster and easier. Sometimes, however, faster is not enough. While a well-designed schema can deliver answers to most queries with reasonable rapidity, some queries will require more complex processing. As data sets grow large, even simple queries may exhibit degraded performance. Luckily, there are solutions to these problems. In this chapter, you will learn how derived tables take information from an existing dimensional schema and restructure it for special purposes. In 15, you will learn how aggregate tables are used to pre-summarize information in large data sets. Both techniques can dramatically boost the performance of a dimensional design, without requiring investment in proprietary hardware or software solutions. Derived schemas store copies of existing dimensional data that has been restructured. These data structures can improve query performance and reduce report development complexity, at the cost of additional work loading and managing them. This chapter highlights some derived structures that have already been encountered, and illustrates four more: Merged fact tables precompute drill-across results, making it easier and faster to compare facts from different fact tables. Pivoted fact tables transpose row-wise data into column-wise data, or vice versa, simplifying the construction process for some kinds of reports. Sliced fact tables contain a subset of rows of the original fact table. They are useful for distributed, departmental, and mobile applications, and may also be used to enforce role-based security. Set operation fact tables store results from comparing two stars with union, intersect, or minus operations, dramatically improving the performance and complexity of reports that require these comparisons.

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These techniques are especially well suited to the design of cubes, as well as stars. In keeping with the approach of this book, most of the principles and examples in this chapter will make use of relational terminology referring to stars, fact tables, and dimension tables. Keep in mind that you can substitute cubes for stars in any of these situations, with powerful results.

Table A-2.

As with the DELETE statement, subqueries can play an important role in the UPDATE statement because they let you select rows to update based on information contained in other tables. Here are several examples of UPDATE statements that use subqueries: Raise by $5,000 the credit limit of any customer who has placed an order for more than $25,000.

For most of the dimensional designs you have seen this far, it has been assumed that data comes from a nondimensional source. In an Inmon-style architecture, data comes from the enterprise data warehouse an integrated repository of atomic data that is not designed dimensionally. In a Kimball-style architecture, or in a stand-alone data mart, data comes directly from operational systems. Derived schemas provide a second layer to the dimensional architecture. They piggyback on existing dimensional structures, rather than drawing data from the original sources. By definition, derived schemas are redundant. Redundancy, however, is not a bad thing in the dimensional world. Derived schemas offer some distinct advantages that are in keeping with the overall purpose of the data warehouse. Of course, this redundancy does have costs as well, requiring the loading and maintenance of new data structures. You have already encountered several forms of derived schemas, including snapshots, accumulating snapshots, and core fact tables.

Appendix:

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The advantages of derived schemas are relatively easy to intuit. Even without formal training, many uses will be patently obvious. Derived schemas can improve query performance and reduce report complexity. Less obvious, but equally important, a derived schema can be used to produce a replica of the base schema that is limited in scope. This helps control the size of data sets in distributed, departmental, or mobile applications, and allows for the application of rolebased security for sensitive data. Lastly, derived schema designs are very handy when deploying OLAP cubes for analytic purposes. Targeted at particular analytic needs, derived designs can produce compact cubes that optimize performance and minimize complexity for specific classes of business questions.