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4 A Fact Table for Each Process 77
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Sales Calls 288 301 229 733 315 322 277 704
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Proposals 101 212 77 279 299 201 104 266
Period Q1 Q1 Q1 Q1 Q2 Q2 Q2 Q2
Region North South East West North South East West
Orders 75 135 60 200 90 140 75 188
Period Q1 Q1 Q1 Q1 Q2 Q2 Q2 Q2
Region North South East West North South East West
Revenue 9,150 16,470 7,320 24,400 10,980 17,080 9,150 22,936
Merge, Compute Yield Ratio (Sales Calls/Orders)
Sales Report 2008 Period Q1 Region North South East West North South East West Sales Calls 288 301 229 733 315 322 277 704 Proposals Orders Revenue Yield 101 212 77 279 299 201 104 266 75 135 60 200 90 140 75 188 9,150 16,470 7,320 24,400 10,980 17,080 9,150 22,936 26% 45% 26% 27% 29% 43% 27% 27%
Figure 4-13
Drilling across four fact tables
Clearly, this sameness of dimensions is crucial for drilling across. It will be formalized and extended in 5, as the principle of conformed dimensions. Conformed dimensions are the key to ensuring that analysis can cross multiple stars or even multiple subject areas. Finally, notice that the drill-across process can be applied to a single star to produce useful comparisons. For example, a this year versus last report might show orders by region for the current period and the same period last year, with a ratio showing the percent increase. This report can be constructed by querying orders by region for this year, and again for last year. The two sets of results can then be joined on region values for a current year versus last comparison.
Drill-Across implementations
The guidelines in Figure 4-12 specify what needs to happen when drilling across. How these steps are carried out is another matter. A number of ways will meet the challenge, whether
Part ii
PART II
Multiple Stars
1 1 RDBMS 2 1 2
Reporting Environment
Figure 4-14 Three ways to drill across
you are writing code by hand, or using a business intelligence tool that automatically generates queries. Three common implementations are depicted in Figure 4-14. The diagram depicts three different ways to drill across. Each begins with a pair of stars at the top of the page and ends with a report at the bottom of the page. The phases of the drill-across operations are indicated by the numerals 1 and 2; intermediate result sets are depicted as gray boxes. It may be helpful to think in terms of the orders and shipments example. Phase 1 involves separate queries for orders and shipments, each grouping results by product. Phase 2 merges these intermediate result sets and produces a ratio for the final report. The diagram also segregates activities based on where they take place: on the database versus within a reporting environment. The database may be a single RDBMS instance or a distributed implementation; the reporting environment may be a simple desktop client or a combination of application servers, web servers, and browsers.
Splitting the Processing
The first implementation approach, on the left side of Figure 4-14, splits the processing between the RDBMS environment and the reporting environment. In the top half of the diagram, two queries are executed: one for each of the stars. Each of these queries retrieves the same dimensions, and aggregates the respective facts to that level.
4 A Fact Table for Each Process 79
The results of these queries are sent to the reporting environment, where Phase 2 will be performed. Whether on a desktop tool or application server, this phase is not performed by the RDBMS. The reporting application merges the result sets in this environment to produce the final report. This approach may be implemented using procedural logic, supported by your reporting tool, or automatically invoked by a business intelligence product (see the sidebar). In each case, it effectively avoids the hazard of joining two fact tables, providing accurate and consistent results. The execution of Phase 2 outside the realm of the database is often criticized as inefficient. Detractors point out that information is moved across the network to an application server to be joined, despite the fact that the RDBMS is specifically designed to join data sets. Notice, however, that the join process in Phase 2 is a full outer join. This process requires each data set to be sorted consistently, and then merged together. If the DBMS is asked to sort the data before it is forwarded to the application server, all that remains is the merge, which is relatively simple. In fact, performance gains may be realized by performing this merge without the overhead of the RDBMS.
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