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In a report, dimensions also serve to specify groupings or break levels, or to identify levels of subtotals. This can be seen in the orders report, where category is used to break up the report into sections; individual categories are also used to label rows containing subtotals. These features are clues that category is a dimension. Dimensions can also be identified by their use in controlling the aggregation of measurements. In the orders report, several measurements are specified for each product name and SKU. (SKU is a retail term that identifies an individual product, or stock keeping unit. ) Product and SKU are dimensions. Notice, too, that dimension values may be used to sort data. Facts can also be spotted based on their use in queries or reports. Elements that are aggregated, summarized, or subtotaled are facts. Clearly, the report in Figure 1-2 contains the facts cost and order dollars. These numbers are being specified at various levels, including products, categories, and a grand total. Look again, and you may notice another fact: quantity ordered. Although it is not being subtotaled, this number is presumably being aggregated. In this case, it looks like quantity ordered by product is being aggregated from individual orders. The style of the report in Figure 1-2 is sometimes referred to as tabular. Other reports may cross-reference facts and dimensions in matrix format, or even display facts graphically. The values being plotted are facts; axis values, bar labels, and qualifications are dimensions. In some cases, it can be hard to tell whether a data element is a fact or a dimension. Sometimes, a single data element can be used in both ways. 6, More on Dimension Tables, discusses behavioral attributes, which are dimensions that have been derived from facts. Later, 11, Transactions, Snapshots, and Accumulating Snapshots, looks at techniques used to construct facts out of dimension values. It is also possible to find a process where there does not appear to be a clear measurement. 12, Factless Fact Tables, describes how the factless fact table can be used to deal with this kind of situation. You also may find that some important business metrics are not so easily rolled up. 3, Stars and Cubes, considers nonadditive facts, while 11 discusses the curious case of semi-additive facts.
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A dimensional design organizes facts and dimensions for storage in a database. It is common for a set of dimensions to share relationships to one another, independent of facts. These are grouped together in a single table to reflect their natural clustering. Similarly, facts that are available at the same level of detail are grouped together. For example, suppose a study of the orders process reveals the facts and dimensions shown in Figure 1-3. In this example, products are being sold by a distributor to retail stores, so the customers are companies. Each of the facts listed in the column on the left may be rolled up according to the dimension values in the right-hand column. Notice natural affinities exist among the dimension values. Some of them clearly belong together. For example, product and product description both seem to be associated with SKU. Salesperson name is determined by salesperson ID. Other dimensions are more distantly associated. Products, for example, have brands. Salespeople work in regions. Figure 1-4 shows how the facts and dimensions for the orders process might be grouped together.
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1 Analytic Databases and Dimensional Design 9
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Facts and dimensions for the orders process
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The diagram in Figure 1-4 depicts measurement of the orders process. It identifies the important measurements for the process and shows the dimensional context in which those measurements are evaluated. The facts and dimensions in this diagram can be combined in a number of ways, answering a wide array of business questions. Take any fact (or facts), add the word by, and then any combination of dimensions. The diagram can be easily understood by a businessperson. Things I measure go in the middle; the ways I look at them go in the surrounding boxes, loosely grouped or categorized. The dimensional framework for the orders process in Figure 1-4 can easily be translated into a database design, or more specifically a dimensional design. A dimensional design organizes facts and dimensions for storage in a database management system. In a relational database management system (RDBMS), the design is referred to as a star schema. In a multidimensional database (MDB), the design is referred to as a cube. This chapter introduces the star schema; we will return to cubes in 3.
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