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Part I
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Using these generic definitions, it will be possible to address dimensional design techniques in a manner befitting any data warehouse architecture, including yours.
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Dimensional design figures into data warehouse architectures in very different ways. Inmon s Corporate Information Factory consolidates information from throughout the enterprise into a central repository called an enterprise data warehouse. It is characterized by a third normal form design, and it is not queried directly by warehouse applications. Outward from this hub radiate data marts, each tailored to the needs and viewpoint of a particular business group. These data marts sport a dimensional design and are queried by data warehouse applications. Kimball s dimensional data warehouse also has an enterprise focus. It brings together data from throughout the enterprise into a central repository called the dimensional data warehouse. This repository is characterized by a dimensional design and may be queried directly. A data mart is a logical construct, or subset of the tables. Optionally, separate dimensional structures may be built to support departmental needs. The stand-alone data mart addresses the needs within a subject area, without an enterprise context. It may leverage dimensional design, or it may follow other techniques. The limitations of this approach may be accepted by an organization in a trade-off for rapid access to results and reduced costs. However, they should not be interpreted as indicating shortcomings of data marts or dimensional design. Each of these architectures has a place for the star schema. Generalized definitions for key terms like data warehouse and data mart allow this book to deal with dimensional design in a manner that is applicable to all architectures.
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The quotation from Bill Inmon comes from his article The Problem with Dimensional Modeling, DM Review, May 2000. Much has been written on Inmon s Corporate Information Factory architecture. The most comprehensive coverage is provided in the book The Corporate Information Factory, Second Edition by W. H. Inmon, Claudia Imhoff, and Ryan Sousa (Wiley, 2000). Ralph Kimball s dimensional data warehouse architecture is described in 1 of The Data Warehouse Toolkit, Second Edition, by Ralph Kimball and Margy Ross (Wiley, 2002). It also discusses some of the myths about star schema covered in this chapter, as well as others. Although Kimball does not dedicate an entire book to his architecture, the basic philosophy permeates The Toolkit. Additional information can be found in The Data Warehouse Lifecycle Toolkit, Second Edition, by Ralph Kimball, Margy Ross, Warren Thornthwaite, Joy Mundy, and Bob Becker (Wiley, 2008). Because it is not a formal architecture, there is no book to describe the stand-alone data mart. Any discussion of implementation in a single subject area can be considered data-martcentric. Individual subject areas from The Data Warehouse Toolkit, implemented in the absence of an enterprise context, would fit the bill. Similarly, Data Warehouse Design Solutions by Chris Adamson and Mike Venerable (Wiley, 1998) provides dimensional designs for various warehouse subject areas.
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Now that you have the basic concepts of measurement under your belt and have reviewed the various ways dimensional design can be employed, you are ready to learn the fundamentals of dimensional design. This chapter covers basics in four categories: dimension table features, fact table features, slowly changing dimensions, and cubes. As you learned in 1, Analytic Databases and Dimensional Design, surrogate keys are employed to identify rows in dimension tables. This chapter explores the reasons for this practice. You have also learned that, as the providers of measurement context, dimension tables play an important role in the star schema. This chapter provides techniques to enrich the assortment of dimensions available and examines why you should not shy away from redundancy. Next, this chapter examines the fact table. As a representation of a business process, it is crucial that all relevant measurements be represented, even where some appear redundant. Many important business indicators, however, don t take kindly to being aggregated. This chapter will look at how to handle these nonadditive facts. You will also learn how to set the grain of a fact table, why we call it sparse, and when to use the amusingly named degenerate dimensions. The power of data warehousing stems in part from its ability to provide access to historic data. The data warehouse must be able to respond to changes to information in a way that does not disrupt the ability to study history. Dimensional designs deal with this issue through a series of techniques collectively known as slowly changing dimensions. This chapter will explore the three primary ways to handle change and when it is appropriate to use each. Last, this chapter describes the implementation of a dimensional design in a multidimensional database, where it is known as a cube. Stars and cubes will be contrasted, and you will learn the different ways the cube can be incorporated into a dimensional architecture.
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