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2008 X X X X 2008 2008 XX XX XX XX 1 2 3 REGIONS Table (3 rows) East West Central X X X XX XX XX X X X
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However, a single cube can only show three dimensions. To handle additional dimensions, you have to visualize additional cubes. The N-cube in Figure 21-2 shows the REGIONS, CATEGORIES, and MONTHS dimensions. To represent the SUPPLIERS dimension, the N-cube needs to be replicated 50 times, once for each possible supplier. Furthermore, to represent the CUSTOMERS dimension, we need to replicate those 50 cubes 300 times, one for each possible customer (15,000 cubes in all). Fortunately, some multidimensional DBMSs can present cubes for analysis without needing to physically store each cube that might be required for presentation. The fact table typically contains only a few columns, but many rows tens or hundreds of millions or even billions of rows are not unusual in a production data warehouse. The fact column almost always contains numeric values that can be accumulated, 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.
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SQL and Data Warehousing
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 2008, by region.
SELECT FROM WHERE AND AND AND ORDER SALES_AMOUNT, REGION SALES, REGIONS MONTH = 'January' YEAR = 2008 PROD_TYPE = 'Clothing' SALES.REGION = REGIONS.REGION BY REGION;
Show the average sales for each CUSTOMER, by SUPPLIER, for each month.
SELECT FROM WHERE AND GROUP ORDER AVG(SALES_AMOUNT), CUST_NAME, SUPP_NAME, MONTH, YEAR SALES, CUSTOMERS, SUPPLIERS SALES.CUST_CODE = CUSTOMERS.CUST_CODE SALES.SUPP_CODE = SUPPLIERS.SUPP_CODE BY CUST_NAME, SUPP_NAME, MONTH, YEAR BY CUST_NAME, SUPP_NAME, MONTH, YEAR;
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: Sales data may in fact be accumulated for each sales office. Each office is a part of a sales district, and each district is a part of a sales region. Sales data is accumulated by month, but it may also be useful to look at quarterly sales results. Each month is a part of a particular quarter. Sales data may be accumulated for individual products ordered, and the products are associated with a particular supplier. Multilevel dimensions such as these complicate the basic star schema, and in practice, you can deal with them in several ways: Additional data in the dimension tables The geographic dimension table REGIONS might contain information about individual offices, but also include columns indicating the district and region to which the office belongs. Aggregate data for these higher levels of the geographic dimension can then be obtained by summary queries that join the fact table to the dimension table and can be filtered based on the district or region columns. This approach is conceptually simple, but it means that all aggregate (summary) data must be calculated query by query. This likely produces unacceptably poor performance.
PART VI
Part VI:
SQL Today and Tomorrow
Multiple levels within the dimension tables The geographic dimension table might be extended to include rows for offices, districts, and regions. Rows containing summary (total) data for these higher-level dimensions are added to the fact table when it is updated. This solves the runtime query performance problem by precalculating aggregate (summary) data. However, it complicates the queries considerably. Because every sale is now included in three separate fact table rows (one each for office, district, and region), any totals must be computed very carefully. Specifically, the fact table must usually contain a level column to indicate the level of data summarization provided by that row, and every query that computes totals or other statistics must include a search condition that restricts it to rows at only a specific level. Precomputed summaries in the dimension tables Instead of complicating the fact table, summary data could be precomputed and stored in the dimension tables (for example, summary sales for a district could be stored in the district s row of the geographic dimension table). This solves the duplicate facts problem of the previous solution, but it works only for very simple precomputed amounts. The precalculated totals don t help with queries about products by district or about district results by month, for example, without further complicating the dimension tables. Multiple fact tables at different levels Instead of complicating the fact table, this approach creates multiple fact tables for different levels of summary data. To support cross-dimension queries (for example, district results by month), specialized fact tables that summarize data on this basis are needed. The resulting pattern of dimension tables and fact tables tends to have many interrelationships, creating a pattern resembling a snowflake; hence, this type of schema is often referred to as a snowflake schema. This approach solves the runtime performance problem and eliminates the possibility of erroneous data from a single fact table, but it can add significant complexity to the warehouse database design, making it harder to understand. Furthermore, many of the popular data analysis tools cannot handle snowflake schemas. In practice, finding the right schema and architecture for a particular warehouse is a complicated decision, driven by the specifics of the facts and dimensions, the types of queries frequently performed, and other considerations. Many companies use specialized consultants to help them design data warehouses and deal with exactly these issues.
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