barcode vb.net 2010 PART V in Software

Print QR Code 2d barcode in Software PART V

PART V
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Performance
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These techniques are especially well suited to the design of cubes, as well as stars. In keeping with the approach of this book, most of the principles and examples in this chapter will make use of relational terminology referring to stars, fact tables, and dimension tables. Keep in mind that you can substitute cubes for stars in any of these situations, with powerful results.
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Restructuring Dimensional Data
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For most of the dimensional designs you have seen this far, it has been assumed that data comes from a nondimensional source. In an Inmon-style architecture, data comes from the enterprise data warehouse an integrated repository of atomic data that is not designed dimensionally. In a Kimball-style architecture, or in a stand-alone data mart, data comes directly from operational systems. Derived schemas provide a second layer to the dimensional architecture. They piggyback on existing dimensional structures, rather than drawing data from the original sources. By definition, derived schemas are redundant. Redundancy, however, is not a bad thing in the dimensional world. Derived schemas offer some distinct advantages that are in keeping with the overall purpose of the data warehouse. Of course, this redundancy does have costs as well, requiring the loading and maintenance of new data structures. You have already encountered several forms of derived schemas, including snapshots, accumulating snapshots, and core fact tables.
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Uses for Derived Schemas
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The advantages of derived schemas are relatively easy to intuit. Even without formal training, many uses will be patently obvious. Derived schemas can improve query performance and reduce report complexity. Less obvious, but equally important, a derived schema can be used to produce a replica of the base schema that is limited in scope. This helps control the size of data sets in distributed, departmental, or mobile applications, and allows for the application of rolebased security for sensitive data. Lastly, derived schema designs are very handy when deploying OLAP cubes for analytic purposes. Targeted at particular analytic needs, derived designs can produce compact cubes that optimize performance and minimize complexity for specific classes of business questions.
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Query Performance
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A well-designed database schema can usually answer a large percentage of business questions in a reasonable response time. For the remaining minority of questions, however, it may perform poorly. This is true of any kind of design, dimensional or otherwise. It is an example of the famous 80/20 rule at work: any design will meet 80 percent of the needs smoothly; the remaining 20 percent will be more difficult. Although numbers may not always be the same, the principle applies. Derived schemas can address performance issues of the minority queries those for which the original dimensional design is not optimal. Data is reorganized into a format that is
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14 Derived Schemas 327
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better suited to answer the question. For example, it can be costly to perform a subquery or set operations when comparing information from more than one fact table. By precomputing the operation and storing the result in a new fact table, performance is enhanced. The techniques in this chapter provide performance benefits for specific classes of query. Merged fact tables eliminate the need to use the multi-step drill-across process when comparing business processes. Pivoted fact tables eliminate the need to transpose rows and columns when reports require it. Set operation fact tables eliminate the need to perform subqueries or leverage SQL set operators. In each case, the derived star is specifically adapted to a specific kind of query.
Report Complexity
Performance is not the only area in which business questions are not created equal. As IT managers are well aware, reports vary in terms of development cost as well. One schema design may deliver sufficient performance for all queries but render a subset of reports very difficult to produce. This complexity can become a concern for two reasons. First, it will be difficult or impossible for end users or analysts to develop these problem reports. They will have to be handled by trained developers or allowed to go unanswered. The former will introduce a lag between question and answer and introduces backlog; the latter will cast the pall of failure over the data warehouse as a whole. Second, complex reports can cost significantly more to develop. While performance may be acceptable, the necessary work involved in producing the report takes longer, leverages advanced or obscure features of the reporting tools, requires additional skill, and is harder to test and modify. For example, a particular business question might require the development of a report that takes query results, parses them into a set of variables, applies layers of conditional logic, and transposes data. Even if it performs well, this kind of report requires more time and skill to produce, test, and maintain. When a large number of important questions are rendered complicated to answer, a derived schema may alleviate these problems. By restructuring data, a derived schema makes answers accessible to users of less skill, and may also reduce report development costs.
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