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Special row for use with orders
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Same old problem: These zeros will cause trouble
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Figure 4-5 Facts with different grain in a single table
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highlighted in the diagram, happens to have the surrogate key value 0. This key value is used in the first three rows of the fact table, for situations where there is an order but no shipment. Now consider how this multi-process fact table behaves when a user analyzes a singleprocess: shipments. The sample report in Figure 4-6 shows quantity shipped by product and shipper for customer 777. Again, it is not customary to study key values; normally you would expect to see natural keys or product names. As in the previous example, this report has a confusing preponderance of rows with the value 0. In addition, these rows contain the cryptic shipper name [ not a shipper ]. As before, the presence of these rows may be confusing to business users, who note that other products which did not ship do not appear on the report. This situation can be dealt with using the same techniques described previously: adding a having clause, or generalizing the facts. As before, the result is undue complication for the reporting effort when a single process is being studied. NoTe The situation in this example becomes exacerbated when orders and shipments occur on the same day for the same product and customer. In this case, it becomes necessary to store two rows. One corresponds to the orders, which have no attendant shipper data, and the other is for the shipments, which have no order data. In fact, orders and shipments will never be recorded in the same fact table row!
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4 A Fact Table for Each Process 69
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Shipment Report by Shipper January 2008 Customer 777 Product Product 111 Product 111 Product 222 Product 222 Product 222 Product 333 Shipper Name [ not a shipper ] Zig-Zag Inc. [ not a shipper ] Zig-Zag Inc. US Ship Co. [ not a shipper ] Page 1 of 1 Quantity Shipped 0 100
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75 125 0
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Figure 4-6 A report that focuses on shipping has confusing data
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An alternative single-fact table design allows NULL keys for fact table rows with no shipper. Unfortunately, this approach further complicates the reporting challenges. The use of NULL keys does cause the extraneous rows to disappear from the shipper report, as shown in the first report in Figure 4-7. This works because the shipper dimension has been included in the report. Unfortunately, the second report in the figure shows that, when the shipper table is not involved in the query, zero-valued facts return. This occurs because there is no join from the fact table to the shipper dimension. TiP When two or more facts have different grain, they represent different processes. Placing them together in a single fact table will hamper analysis of the individual processes. Further complicating matters, allowing NULL values for the shipper_key in the fact table requires a different join configuration when studying orders and shipments together. In such a case, it would be necessary to perform an outer join, so as not to omit the rows with orders and no facts. This outer join, in turn, would make it difficult to qualify for a particular shipper type. NoTe Do not generalize this example to assume that optional relationships are the problem. In this example, problems occur because the facts have different grain. One fact involves shipper, the other does not. There are cases when a single fact has an optional relationship to a dimension. For example, some orders may have supervisory approval, while others do not. This supervisory involvement does not affect the grain of the fact. Strategies for dealing with optional relationships are discussed in 6, More on Dimension Tables.
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Part ii
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PART II
Multiple Stars
SALES_FACTS customer_ product_ shipper_ quantity_ quantity_ key key key ordered shipped day_key 123 123 123 456 456 789 777 777 777 777 777 777 111 222 333 111 222 222 (NULL) (NULL) (NULL) 9999 9999 8888 100 200 50 0 0 0 0 0 0 100 75 125
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