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I 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. I 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. I 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, there are several ways to deal with them: I Additional data in the dimension tables. The geographic dimension table 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 selected 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. I 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. I Precomputed summaries in the dimension tables. Instead of complicating the fact table, summary data may be precomputed and stored in the dimension tables (for example, summary sales for a district 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 district results by month, for example, without further complicating the dimension tables. I 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,
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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. 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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With a star-schema structure, a relational database conceptually provides a good foundation for managing data for business analysis. The capability to freely relate information within the database based solely on data values is a good match for the ad hoc, unstructured queries that typify business intelligence applications. But there are some serious mismatches between typical business intelligence queries and the capabilities of the core SQL language. For example: I Data ordering. Many business intelligence queries deal explicitly or implicitly with data ordering they pose questions like What is the top 10 percent What are the top 10 or Which are the worst performing As a set-oriented language, SQL manipulates unordered sets of rows. The only support for sorting and ordering data within standard SQL is the ORDER BY clause in the SELECT statement, which is applied only at the end of all other set-oriented processing. I Time series. Many business intelligence queries compare values based on time contrasting this year s results to last year s, or this month s results to the same month last year, or computing year-over-year growth rates, for example. It is very hard, and sometimes impossible, to get side-by-side comparisons of data from different time periods within a single row of standard SQL query results, depending on the structure of the underlying database. I Comparison to aggregate values. Many business intelligence queries compare values for individual entities (for example, office sales results) to an overall total, or to subtotals (such as regional results). These comparisons are difficult to express in standard SQL. A report format showing line-item detail, subtotals, and totals is impossible to generate directly from SQL, since all rows of query results must have the same column structure. To deal with these issues, DBMS products on data warehousing have tended to extend the core SQL language. For example, the DBMS from Red Brick, one of the data warehousing pioneers and now a part of Informix s product line (which has, in turn,
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