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The idea in those days, however, was that if management could be successful in one business, why not in two, three, four, or more diverse areas By the early 1980s, however, it had become apparent that this strategy did not work A single management structure was less effective in running operations across several industries than separate leadership teams The individual parts were worth more than the whole In the case of the former Borg Warner Corp, it began shedding underperforming assets after the leveraged buyout in 1987 By 1993 two separate entities remained: Borg-Warner Automotive and BorgWarner Securities Each went public in separate transactions in 1993..

Developers can sometimes minimize the impact of a type 1 change if the rollup dimension also includes a type 2 attribute that the type 1 attribute describes. In this case, the type 1 change may be applied only to the appropriate row, and aggregate facts will not require an update. Designers sometimes borrow language from entity-relationship modelers to describe this case: the type 1 attribute in the aggregate dimension is fully dependent on the type 2 attribute. This approach, however, provides new challenges for the ETL process. See Type 1 Complications in 3 for more details.

While this book uses relational designs to illustrate most concepts, 3 pointed out that this is simply a convenience. Most of the principles of dimensional modeling apply equally well, whether implemented in a star schema or a cube. The benefit of summarization, however, differs when it comes to cubes.

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The multidimensional cube is intended as a high-performance data structure. Generally, cubes achieve near-instantaneous performance by precomputing the value of facts across the various members of each dimension. This permits the interactive analysis style for which OLAP is famous. This also means there may be little performance benefit in creating one cube that summarizes another. TiP When a cube is used as primary storage for a dimensional design, the data set is already optimized for high performance. A summary cube is not necessary. Of course, there is an exception to every rule. Vendor innovations that are intended to improve the scalability of the cube are also bringing back the value of summarizing it. As noted in 3, the cube has traditionally been less scalable than a star schema. Because of the need to precompute facts across each member, cubes grow exponentially larger as more and more dimensions are added. To cope with this drawback, some products now allow an administrator to control the degree of pre-summarization within a cube. This allows the cube to contain more data but also reduces its performance. In this case, additional cubes may be useful as high-performance summaries of the base cube. The two stars in Figure 15-1 might easily be replaced with cubes. The base cube, which contains orders by order line, may be configured to maximize storage efficiency of the facts. The aggregate cube, which contains orders by month, product, and salerep, can be configured to maximize performance.

Many architectures incorporate both the star schema and the cube. As noted in 3, the star scales well, while the cube performs well. The best of both worlds is achieved by relying on the star to store granular, detailed data, with cubes containing high-performance extracts. In this scenario, one or more cubes summarize data stored in a base star. Queries and reports are designed to make use of the cubes, to afford optimum performance. In Figure 15-3, for example, cubes might replace one or all of the aggregate stars.

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