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The structure (schema) of a warehouse database is typically designed to make the information easy to analyze, since that is the major focus of its use. The structure must make it easy to slice and dice the data along various dimensions. For example, one day a business analyst may want to look at sales by product category by region, to compare the performance of different products in different areas of the country. The next day, the same analyst may want to look at sales trends over time by region, to see which regions are growing and which are not. The structure of the database must lend itself to this type of analysis along several different dimensions.
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In most cases, the data stored in a warehouse can be accurately modeled as an N-dimensional cube (N-cube) of historical business facts. A simple three-dimensional cube of sales data is shown in Figure 21-2 to illustrate the structure. The fact in each cell of the cube is a dollar sales amount. Along one edge of the cube, one of the dimensions is the month during which the sales took place. Another dimension is the region where the sales occurred. The third dimension is the type of product that was sold. Each cell in the cube represents the sales for one combination of these three dimensions. The
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$50,475 amount in the upper-left front cell represents the sales amount for January, for clothing, in the East region. Figure 21-2 shows a simple three-dimensional cube, but in many warehousing applications, there will be a dozen dimensions or more. Although a twelvedimensional cube is difficult to visualize, the principles are the same as for the three-dimensional example. Each dimension represents some variable on which the data may be analyzed. Each combination of dimension values has one associated fact value, which is typically the historical business result obtained for that collection of dimension values. To illustrate the database structures typically used in warehousing applications, we use a warehouse that might be found in a distribution company. The company distributes different types of products, made by various suppliers, to several hundred customers located in various regions of the country, through the efforts of its sales force. The company wants to analyze historical sales data along these dimensions, to discover trends and gain insights that will help it better manage its business. The underlying model for this analysis will be a five-dimensional fact cube with these dimensions: I Category. The category of product that was sold, with values such as clothing, linens, accessories, and shoes. The warehouse has about two dozen product categories.
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I Supplier. The supplier who manufactures the particular product sold. The company might distribute products from 50 different suppliers. I Customer. The customer who purchased the products. The company has several hundred customers. Some of the larger customers purchase products centrally and are serviced by a single salesperson; others purchase on a local basis and are served by local salespeople. I Region. The region of the country where the products were sold. Some of the company s customers operate in only one region of the country; others operate in two or more regions. I Month. The month when the products were sold. For comparison purposes, the company has decided to maintain 36 months (three years) of historical sales data in the warehouse. With these characteristics, each of the five dimensions is relatively independent of the others. Sales to a particular customer may be concentrated in a single region or in multiple regions. A specific category of product may be supplied by one or many different suppliers. The fact in each cell of the five-dimensional cube is the sales amount for that particular combination of dimension values. With the attributes just described, the fact cube contains over 35 million cells (24 categories 50 suppliers 300 customers 3 regions 36 months).
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