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Creating QR in Software Derived Schemas 329

14 Derived Schemas 329
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Accumulating Snapshots
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An accumulating snapshot is another example of a derived schema. As you learned in 11, it is often useful to study the time between significant events. This may require correlating activities across multiple stars, such as orders, shipments, and payments, or across multiple rows in a single star, such as the submission, approval, and settlement on mortgage applications. In either case, understanding the average time at each stage may require aggregating a very large number of correlations. This can be an extremely intensive and poorly performing process. The accumulating snapshot represents various process steps in a single fact table row. By correlating activities in advance, it eliminates the need for complex correlations at query time. This makes the accumulating snapshot particularly well suited to the study of time spent at various processing stages. The accumulating snapshot is almost always derived from one or more stars. The original stars remain useful in supporting analytic activities that are focused on individual tasks. The accumulating snapshot is called on in situations that require aggregating correlated activities. Its design is specifically adapted to these particular questions.
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Core Fact Tables
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13 described the use of core and custom stars as a means for coping with heterogeneous attributes. When there is type-specific variation in the facts and dimensions associated with a process, custom stars permit detailed analysis of each type, while core stars permit analysis across all types. For example, the dimension attributes and facts associated with an orders process may vary with the kind of thing being ordered. Product types might include books, compact discs, and subscriptions. Certain dimension attributes are common, such as the product category and name, and at least one common fact order dollars. Other attributes, such as the publication format or record label, are specific to particular types. As 13 pointed out, the custom fact tables are often built first. The core fact table, then, becomes a union all of the common facts. This approach is very helpful when the various custom types correspond to different sources, allowing the ETL routines that load each fact table to focus on a single source. The core fact table is then derived from the custom, or type-specific, fact tables. As with any other form of derived schema, this arrangement allows different structures to serve different analytic purposes. Analysis that is confined to a particular type may focus on a custom star, with full access to the unique dimension attributes or facts associated with the particular type. Analysis across all types focuses on the core star, with integrated access to all common attributes.
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The Cost of Derived Schemas
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It is important to recognize that a derived schema does not achieve these benefits at no cost. Benefits are achieved by moving work out of the query and reporting process and into the ETL process. This is in keeping with the overall objectives of data warehousing but must be factored into design decisions. The presence of derived schemas has another impact as well. Developers of queries and reports are now faced with multiple representations of the same process and must choose
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Part V
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PART V
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Performance
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the appropriate dimensional structure for each business question. This potentially limits the understandability of the solution.
eTL Processing
Data warehouses are designed and built to answer questions about business processes. The data warehouse assembles a repository of data that is drawn from throughout the business, integrates it, and organizes it for analytics. By doing this heavy lifting in advance, the questions are easier to answer. The same principle applies to the development of derived schemas. The benefits they achieve do not come for free. Additional heavy lifting is added to the ETL process. Each derived schema must be loaded and maintained by ETL developers and tuned by database administrators. This additional work at load time buys improved performance or reduced complexity at query time. It is important not to lose sight of the additional work that will be required by derived tables. More tables must be designed, loaded, and maintained. More ETL routines must be developed, tested, and deployed. More dependencies will be added to a load window that may already be fully utilized. More processing resources may be required to crunch the data during load time. These impacts must be balanced with the benefits of the derived schema. While additional load-time activity is consistent with the fundamental principles of data warehousing, there must be sufficient business benefit to justify it. As you will learn in 18, How to Design and Document a Dimensional Model, the best way to make this kind of design decision involves representatives of a variety of points of view, including report developers, ETL developers, database administrators, end users, and IT management.
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