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In most data warehouses, the most effective way to model the N-dimensional fact cube is with a star schema. A star schema for the distributor warehouse in the previous example is shown in Figure 21-3. Each dimension of the cube is represented by a dimension table. There are five of them in the figure, named CATEGORIES, SUPPLIERS, CUSTOMERS, REGIONS, and MONTHS. There is one row in each dimension table for each possible value of that dimension. The MONTHS table has 36 rows, one for each month of sales history being stored. Three regions produce a three-row REGIONS table. Dimension tables in a star schema often contain columns with descriptive text information or other attributes associated with that dimension (such as the name of the buyer for a customer, or the customer s address and phone number, or the purchasing terms for a supplier). These columns may be displayed in reports generated from the database. A dimension table always has a primary key that contains the value of the dimension. If the values of a dimension are numbers (such as a clothing size) or short text strings (such as a city name), the primary key may be this dimension value itself. It s more common for dimension values to be expressed in some type of code value. Three-letter airport codes and customer numbers are typical examples. In the sample warehouse of Figure 21-3, we assume that actual values are used as primary keys for REGIONS (East, West, and so on), CATEGORIES (clothing, shoes, and so on), and MONTHS. The other two dimensions use coded values (CUST_CODE for CUSTOMERS, SUPP_CODE for SUPPLIERS).
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The largest table in the database is the fact table in the center of the schema. This table is named SALES in Figure 21-3. The fact table contains a column with the data values that appear in the cells of the N-cube in Figure 21-2. In addition, the fact table contains a column (or columns) that forms a foreign key for each of the dimension tables. In this example, there are five such foreign key columns. With this structure, each row represents the data for one cell of the N-cube. The foreign keys link the row to the corresponding dimension table rows for its position in the cube. The fact table typically contains only a few columns, but many rows hundreds of thousands or even millions of rows are not unusual in a production data warehouse. The fact column almost always contains numeric values, such as currency amounts, units shipped or received, or pounds processed. Virtually all reports from the warehouse involve summary data totals, averages, high or low values, percentages based on arithmetic computations on this numeric value. The schema structure of Figure 21-3 is called a star schema for obvious reasons. The fact table is at the center of a star of data relationships. The dimension tables form the points of the star. The relationships created by the foreign keys in the fact table connect the center to the points. With the star-schema structure, most business analysis questions turn into queries that join the central fact table with one or more dimension tables. Here are some examples: Show the total sales for clothing in January, by region.
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SELECT FROM WHERE AND AND ORDER SALES_AMOUNT, REGION SALES, REGIONS MONTH = 01/1999 PROD_TYPE = "Clothing" SALES.REGION = REGIONS.REGION BY REGION
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Show the average sales for each CUSTOMER, by SUPPLIER, for each month.
SELECT FROM WHERE AND GROUP ORDER AVG(SALES_AMOUNT), CUST_NAME, SUPPLIER_NAME, MONTH SALES, CUSTOMERS, SUPPLIERS SALES.CUST_CODE = CUSTOMERS.CUST_CODE SALES.SUPP_CODE = SUPPLIERS.SUPP_CODE BY CUST_NAME, SUPP_NAME BY CUST_NAME, SUPP_NAME, MONTH
Multilevel Dimensions
In the star-schema structure of Figure 21-3, each of the dimensions has only one level. In practice, multilevel dimensions are quite common. For example:
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