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answer all questions, although not with optimum performance. Developers, on the other hand, can be trained to select from among the base star and corresponding aggregates. They are exposed to the entire portfolio of aggregates, as depicted in Figure 15-3. Even this situation is not optimal. The reports written by end users do not achieve any of the performance advantages offered by the aggregates. Any reports written by developers are hard-coded to use a particular aggregate. If a new aggregate is added that would offer better performance for a particular report, it must be rewritten to take advantage of this possibility. Conversely, if an aggregate is removed, then any reports that were written to take advantage of it will be broken. They will not work until they are rewritten to make use of one of the remaining stars. As you will see later in this chapter, many software products offer features that seek to address these shortcomings. These tools can be configured to redirect queries automatically based on available aggregates. A variety of different approaches to this task have evolved; the function is referred to generically as aggregate navigation. In some cases, these tools may also offer assistance in the creation and maintenance of the aggregate tables.

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Before an aggregate can be used, it must be built. As with the derived tables of 14, this is the price we must pay for increased performance. Designed around the principle of performance, it makes sense to source aggregate tables from the original star. The occurrence of a type 1 change can make it quite difficult to maintain aggregates incrementally, leading many developers to adopt a drop-and-rebuild approach.

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By allowing the concept of conformance to guide aggregate design, we guarantee consistent results from the summary, while keeping the process of rewriting queries as simple as possible This same guideline has one additional advantage: it helps keep the ETL process simple as well Construction of conformed dimensions was discussed in 5 If a dimension table is shared by the base star and aggregate, only one version exists; no additional work is required at ETL time If the aggregate uses an identical copy of the original dimension, this can be handled via simple replication In the case of a conformed rollup, such as the month table in Figure 15-1, some additional processing will be needed The rollup will summarize attributes of the base dimension table, but it has its own surrogate key.

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As you learned in 5, the best way to load a conformed rollup is to use the base dimension table as its source This eliminates any redundant processing that would be required if the rollup dimension were loaded from the original source More importantly, it eliminates the possibility that processing rules will be applied inconsistently This guarantees consistent structure and content, the twin requirements of conformance The process can be streamlined by adding housekeeping columns to the base dimension that indicate the date the row was created and the date the row was last modified These dates can be used by the process that loads the conformed rollup to identify changes The development of ETL routines for aggregate fact tables is guided by the same principle Sourcing the aggregate from the base star guarantees the conformance that is required, both in terms of structure and in terms of content.

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TiP Source aggregate tables from the base schema to guarantee consistent structure and content Following these guidelines, the overall dependency of table load routines during the ETL process might look like this: 1 Load base dimensions 2 Load conformed rollups 3 Load base fact table 4 Load aggregate fact table Actual dependencies are slightly more relaxed than this For example, after base dimensions have been loaded, it is possible to load the base fact table Unfortunately, there is a complication..

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One potential roadblock stands in the way of this simple workflow: the type 1 change. When base data and aggregates are loaded sequentially, the occurrence of a type 1 change can force the need to reload the aggregate completely, rather than update it incrementally. To understand why this happens, consider a very simple example. Suppose an aggregate of order_facts contains rows that summarize orders by product manager, which is a type 1 attribute. This aggregate will call for a simple conformed rollup of the product dimension, which includes only the column product_manager.

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