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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.

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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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Large objects allow for storage of data that is considerably larger than the data supported by the data types covered thus far, often several megabytes in size. The handling of large objects is an advanced topic that is beyond the scope of this book, so they are listed here without any syntax details. For now, you only need to know that the DBMSs that support these types usually store the actual data separately from the table that references it, and that data can be moved in and out of the large object in pieces, such as a few kilobytes at a time. All large objects are variable in length by de nition. Consult your DBMS documentation for any special SQL syntax associated with large object types. Character large object A character large object, written in SQL as CLOB. National character large object A character large object that is stored in a particular national language, written in SQL as NCLOB. Binary large object A large object that holds binary data such as an image or sound clip, written in SQL as BLOB.

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.

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.

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