how to make barcode in vb.net 2010 The Database of the Future in Software

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The Database of the Future
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If the previous 15 years are any indication, the next 15 years should bring dramatic changes. The marketplace is on pace to render the storage format of dimensional data irrelevant. A single database management architecture will eventually store the dimensional model in whatever format makes the most sense, whether that is relational tables, a cube, or something entirely different. Users and applications will interact with it in their choice of language, be it SQL or a multidimensional API. Their queries will be rewritten and redirected in real time to suit the underlying storage technique in use. By the time we get there, you may be designing star schemas that are actually stored as cubes, accessing them via SQL, and receiving responses at OLAP speed.
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This chapter covered the basic features of the star schema. This long list of topics can be grouped into four categories. Dimension tables Dimension tables contain natural keys and surrogate keys. This allows the analytic schema to track history independently of the source. Dimension tables should be wide. A rich set of dimensional attributes enables a powerful analytic environment. Columns should be provided for codes and their associated descriptions, concatenated fields as well as their parts, common combinations of values, and descriptive representation of flags. Some dimensions are numeric; they can be distinguished from facts based on how they are used. Dimension tables are not placed in third normal form. Junk dimensions accumulate unrelated dimension attributes. Behavioral dimensions are derived from facts to produce powerful analytic options. Fact tables Fact tables contain compact rows composed of foreign key references to dimensions, and facts. Fact tables should contain all facts relevant to a process, even if some can be computed from others. Nonadditive facts such as ratios should be decomposed into fully additive components, and computed at report creation time. Fact tables are sparse; they record rows only when something happens.
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It is crucial that the grain of a fact table can be stated, either in dimensional terms or with respect to a business term. A dimension stored in a fact table is called a degenerate dimension. This technique is usually reserved for transaction identifiers that exhibit high cardinality. Slow changes The warehouse responds to changes in source data through a process known as slowly changing dimensions. A type 1 slow change overwrites a dimension attribute when its corresponding source changes. The dimension table does not reflect history, and the historic context of existing facts is altered. A type 2 slow change creates a new version of the dimension row when the source value for one of its attributes changes. The dimension table maintains a version history, although it is not tied to time. The historic context of historic facts is preserved. Cubes A dimensional model can also be implemented in a multidimensional database, where it is known as a cube. Cubes enable a fast and powerful form of interaction known as OLAP. The languages that support interaction with cubes support some types of analysis that are hard to express using SQL. Storage requirements increase as dimension attributes are added or the number of transactions increases. Cubes can serve as primary dimensional data stores but have limited scalability. Cubes can serve as a powerful supplement to a star schema, enabling focused and interactive analysis.
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The fundamentals of star schema design presented in this chapter are covered in every book that deals with dimensional design. Briefer treatment of the basics can be found in the author s previous work: Data Warehouse Design Solutions (Adamson and Venerable, Wiley, 1995), and Mastering Data Warehouse Aggregates (Adamson, Wiley, 2006). Both introduce the basics of fact tables and dimension tables in their opening chapters. The concepts of dimension tables, surrogate keys, fact tables, grain, sparsity, and slowly changing dimensions are universally accepted features of dimensional design. They were first codified by Ralph Kimball in magazine articles and later in the first edition of The Data Warehouse Toolkit. Now in its second edition, Ralph Kimball and Margy Ross s The Data Warehouse Toolkit, Second Edition (Wiley, 2002) covers the fundamentals of fact tables and dimension tables in 1. Slow changes are described in 4.
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