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Developers are almost always under pressure to develop a load process that is fast and efficient. For loads that operate in a batch, the available time window is likely to be shared with processes that construct derived and aggregate tables, build cubes, rebuild indexes, execute scheduled reports, and cache their results. For a real-time load, the continuous flow of new transactions results in a similar requirement for processing efficiency. Various kinds of bottlenecks are possible when loading a star schema, but some are fundamental. These result from the need to perform slow change processing. In the worstcase scenario, it is necessary to inspect each source record for new and changed dimensions. Doing so may require as many as three different comparisons to data in the dimension table, as Figure 17-2 illustrated. For example, each time the product dimension is updated, it may be necessary to review all products in the source system for new records (step 1.4), type 1 changes (step 1.5), and type 2 changes (step 1.7). Some of these comparisons, in turn, may be resource-intensive. Finding type 2 changes to a customer record, for example, may require comparing dozens of type 2 attributes. The fundamental bottlenecks of slow change processing can be addressed in two ways: reduce the amount of data to be processed, and simplify the necessary processing. Many asp.net ean 128 EAN - 128 ASP . NET Control - EAN - 128 barcode generator with free ...
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