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People often refer to scalability when talking about the features of a database platform. However, the term itself let alone the actual measurements is open to interpretation. In general, we can define scalability as the ability of database server software to take advantage of upgraded hardware. The law of diminishing returns applies, though. The performance increase of adding a second CPU to a server might not be the 100 percent that one would expect, due to the overhead involved with splitting operations between processors. Many efforts have been made to ensure that the architecture of SQL Server allows for scaling to large databases and making use of advanced options, such as multiple gigabytes of physical memory and parallel processing. When measuring performance between different systems on different platforms, it s important to have a standard test. This test should not be written to prefer any database platform over another and should put a database through some real-world tests that can provide relevant and pertinent information. A real-world attempt at just such a test has been made by the TPM council. You can get more information at www.tpmc.org. There, you ll find the actual benchmarks posted for specific database servers. One important aspect of these benchmarks is that they take the factor of cost into account. Many vendors advertise about the levels of performance that they have been able to attain using the TPM benchmarks. However, this can be largely irrelevant to most businesses, because the costs of such systems are often far out of reach for business users. What is of more interest is how much bang for the buck one can get from a specific platform.

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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.

One way to squeeze the maximum possible performance out of your machines is to use them when they re least busy. This is an important concept in relation to database backups.

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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