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PART V
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Other ways to summarize data that do not meet this chapter s definition of an aggregate include the following: Derived tables, as described in the previous chapter, may provide a useful alternative. A single-table design, on the other hand, is likely to lead to trouble.
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Further Reading
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The basic principles of aggregates are simple and intuitive. As a result, most discussions of dimensional design devote very little time, if any, to the discussion of aggregates. Although relatively simple in concept, incorporating aggregates into your architecture has many implications. Several nuances have only been touched on here. For a complete treatment of aggregation, see Mastering Data Warehouse Aggregates (Wiley, 2006) by Chris Adamson. Kimball and Ross provide a brief description of the principles of aggregation in the context of a clickstream data warehouse in 14 of The Data Warehouse Toolkit, Second Edition (Wiley, 2002). Aggregation strategy is briefly discussed in 16 of The Data Warehouse Toolkit. Some architectures use aggregated data as the basis for a data mart application. In a dimensional data warehouse, aggregates are more akin to an index, summarizing granular data solely for performance reasons. The authors make this important distinction in 4 of The Data Warehouse Lifecycle Toolkit, Second Edition (Wiley, 2008), by Ralph Kimball, Margy Ross, Warren Thornthwaite, Joy Mundy, and Bob Becker. The effect of a type 1 change on an aggregate can be very frustrating, but it is not a shortcoming of the dimensional approach. As Kimball et al. describe in 8 of The Data Warehouse Lifecycle Toolkit, this problem is encountered when using stars, snowflakes, cubes, and even normalized designs. Storing summary and detail data in the same star by leveraging a level column is discussed in further detail in 13 of the first edition of The Data Warehouse Toolkit by Ralph Kimball (Wiley, 1996). Although out of print, this edition may be available in libraries or used bookstores. The topic is also discussed in 3 of Mastering Data Warehouse Aggregates. The additional tables in a snowflake design can provide some reduction in complexity when it comes to aggregates. If the dimension already contains outriggers, then it may not be necessary to construct conformed rollups. An example of an aggregate in a snowflake design is discussed in 8 of Mastering Data Warehouse Aggregates.
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ChAPTeR 16
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Design and Business Intelligence
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PART
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Tools and Documentation
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ChAPTeR 17
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Design and ETL
ChAPTeR 18
How to Design and Document a Dimensional Model
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ChAPTeR
Design and Business Intelligence
A good dimensional design maximizes the potential value of your data warehouse. If it is not easy to access the information, however, even the best dimensional schema becomes a black hole. Luckily, there are a wide variety of software products available that can help make sure this does not happen. Tools that are used to provide information to end users range in functionality from simple report writers to sophisticated business intelligence software products. They can provide information in a numerous formats (charts, tables, dashboard widgets), over a variety of channels (computers, mobile devices, telephones), and according to varied access paradigms (on demand, scheduled, exception alerts). For the sake of simplicity, these tools will be referred to collectively as business intelligence tools. The individual information products they provide to end users will be referred to as reports. As the face of the data warehouse, these tools determine how information can, and cannot, be made available to the people who can use it. Tool features are product-specific, and beyond the scope of this book. However, most of these tools share one thing in common: they do not require the person developing a report to write a query. When configured appropriately, business intelligence tools generate SQL queries for the report developer. The SQL generation capabilities of these products can be a limiting factor for how dimensional data is accessed. Another class of business intelligence tools supports OLAP applications. These tools work with multidimensional cubes rather than relational stars. Like their SQL-based cousins, most of these tools also do not require hand-coding of queries. The capabilities of an OLAP solution stem directly from the depth and breadth of cubes that are made available in the analytic environment. This chapter explores the impact of business intelligence tools on your dimensional designs. You cannot control the query generation capabilities of business intelligence tools in your architecture, but understanding their workings gives you important insight into what kinds of reports can and cannot be produced. Some shortcomings can be addressed with standard dimensional techniques, such as derived tables. Other limitations may be
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