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Sliced fact tables are not always derived from the whole. It is possible to construct the whole as a union-all from the slices, but extreme care must be taken in handling the dimensions. This may occur in situations where regional fact tables are constructed in parallel, then
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combined into a whole at the end of the processing window. Effective derivation of the whole from slices requires a consistent data architecture and a carefully coordinated ETL process. There must be no overlap in the slices. More importantly, each of the slices must share the same common set of dimensions. This implies a load process that handles the following steps sequentially: 1. Process common dimension tables. 2. Process fact table slices, in parallel if desired. 3. Derive combined fact table from slices. Failure to process a common set of dimension tables prior to loading slices generates issues. Problems may include nonconformance, overlapping key values, or inconsistent representation of slow changes. For the solution in Figure 14-3, if the whole were to be derived from the slices, all regions should be working with the same product table, customer table, and so forth. By establishing a unified set of dimensions first, it is possible to load facts from various subsets of data in parallel, while guaranteeing that the resultant slices can be unioned into a single fact table.
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The last form of derived fact table takes two stars as an input and produces an output based on the application of set operations or the use of a subquery or correlated subquery. The resulting star provides performance advantages over the original schema, avoiding the use of set operators at query time.
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As discussed in 3, transaction-grained fact tables are sparse. Rather than record a row for every possible combination of dimension values, they record rows only where an activity or transaction occurs. Order facts, for example, are recorded only when orders take place. Tracking the conditions that may or may not influence activities is done in a factless fact table, as described in 12. For example, the assignment of customers to salespeople can be captured in a coverage star. The fact table contains rows relating customers to salespeople, regardless of whether any orders are placed. Identification of sales assignments that are nonproductive involves comparing order facts with sales assignments. One way to perform this comparison leverages set operations. The set of salesperson/customer assignments for a period, minus the set of salesperson/customer orders for the period, yields nonproductive assignments. As shown in 12, this can be a beastly query to write and may not perform very well. Set operations are not always used for this kind of query, as 12 also noted. Subqueries can also be employed, in conjunction with the EXISTS or NOT EXISTS qualifiers. The end result is no less complex, nor is it likely to exhibit significantly improved performance. Rather than perform this query at runtime, it can be computed during the ETL process, with the result sets stored in a new fact table. Queries and reports no longer need to compute the difference between the sets, as represented by the original fact tables. Instead, they access a derived fact table, using simple straightforward SQL.
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14 Derived Schemas 341
Several Possible Comparisons
Given any two fact tables that share common dimensions, a variety of set operations can be precomputed. Candidates include union, intersect, and multiple minus operations. Not every set will have a use, or even a useful meaning, but if any will be required on a regular basis, a derived fact table may save considerable reporting effort and execution time. TIP Set operation fact tables precompute the comparison of two data sets using set operations or subqueries, storing the result in a new fact table. This greatly simplifies the processing required within queries or reports. The possible set operations involving a pair of fact tables that relate customers and salespeople are depicted in Figure 14-4. Each of the circles represents customer/salesrep
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