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For more information on the design of operational systems, there is no finer reference than Chris Date s An Introduction to Database Systems, Eighth Edition (Addison Wesley, 2003). This book fully explains the principles of normalization used to support transaction processing in a relational database management system. A wealth of information is available on the differences between operational and analytic systems. Two good places to start are 1 of Ralph Kimball and Margy Ross s The Data Warehouse Toolkit, Second Edition (Wiley, 2002) and Inmon, Imhoff, and Sousa s discussion in The Corporate Information Factory, Second Edition (Wiley, 2000). For more information on separating facts from dimensions, you can consult any book on dimensional design. Data Warehouse Design Solutions (Adamson and Venerable; Wiley, 1995) and Mastering Data Warehouse Aggregates (Adamson; Wiley, 2006) both cover the topic in their opening chapters, as does Kimball and Ross s The Data Warehouse Toolkit. These books also cover the prototypical query pattern for a star schema; the browse query is discussed in The Data Warehouse Toolkit.
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There is a wide range of opinion regarding the optimal data warehouse architecture. Opinions are sometimes so strongly held that a colleague of mine often refers to a religious war in data warehousing. That may overstate things, but everyone will agree to this: data warehouse architectures vary widely. One of the ways in which data warehouse architectures diverge is in their use of dimensional design. Some architectures place a heavier emphasis on the star schema, while others use it in a limited capacity. The principles of dimensional design are the same, wherever they are put to use. This book is concerned with these principles. With a diversity of architectures, however, comes confusion. The same terms are used to describe different things. Different terms are used to describe the same thing. Characteristics of one approach are misinterpreted to apply in other situations. In order to understand dimensional design, it is important to clear up this confusion. To do so requires a brief look at data warehouse architecture. This chapter groups data warehouse architecture into three categories. The first two are often called enterprise data warehouse architectures, and are closely associated with W. H. Inmon and Ralph Kimball, respectively. The third does not have a well-known figurehead but is equally common. While these architectures differ in fundamental ways, there is a place for the star schema in each of them. By understanding these approaches, we can avoid misunderstandings in terminology and develop a clear understanding of the capability of the star schema. If you are looking for an answer to the question, What is the best data warehouse architecture you will not find it here. There is no discussion of pros and cons. Nor will you find comprehensive specifications for each architecture. Instead, the objectives for this chapter are simple: 1. To understand each approach at a high level 2. To understand the place of the star schema in each 3. To eliminate some common misconceptions
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PART I Fundamentals
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If you have a data warehouse or analytic application in production, don t expect a direct match with one of these archetypes. Each real-world implementation is different. Yours may contain elements from one or more of these architectures. You should make an effort to understand the alternatives, however. This will give you a better grasp of what is and what is not true about dimensional design.
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In May of 2000, a well-known thought leader in data warehousing had this to say about dimensional design: if I had to design a data mart tomorrow, I would not consider using any other approach. No, it wasn t Ralph Kimball. Those words were written by W.H. Inmon, in an article that appeared in DM Review magazine. Although it is not a surprise to people who follow Inmon s approach to data warehousing, these words never cease to astound adherents of other approaches. I am not sure how we reached this state of affairs, although I suspect that the trade press s desire to contrast Inmon with Kimball is partly to blame. So I will begin by shining some light on Inmon s approach. Bill Inmon is a prolific writer and contributor to the data warehousing community. Through hundreds of articles and dozens of books, he has developed and shared an approach to data warehousing that he calls the Corporate Information Factory. This hub-and-spoke architecture is common, even in IT shops that do not attribute their architecture to Inmon. A highly simplified depiction of the Corporate Information Factory appears in Figure 2-1. Some liberties have been taken, removing numerous components that are not relevant to this discussion and using some generic terminology. For the purpose of understanding the underlying data architecture, and the place of the star schema in it, this diagram is true to Inmon s approach. To understand this architecture, start by looking at the left side of the diagram. There, you will find the operational systems, or transaction systems, that support the business. The data stores associated with these systems may take a number of different forms, including hierarchical data, relational data, and even simple spreadsheets. For the sake of simplicity, only four operational systems are depicted. In the real world, any organization s portfolio of operational systems is sure to be significantly larger. These systems feed a process labeled ETL for extract, transform, load. This process consolidates information from the various operational systems, integrates it, and loads it into a single repository called the enterprise data warehouse. This processing step is nontrivial. It may require accessing information in a variety of different formats, resolving differing representations of similar things, and significant restructuring of data. Some organizations refer to this process as data integration. It may be a batch process that runs periodically or a transaction-based process that occurs in near real time. The final result is the same: the enterprise data warehouse. The enterprise data warehouse is the hub of the corporate information factory. It is an integrated repository of atomic data. Integrated from the various operational systems, it contains a definitive and consistent representation of business activities in a single place. Atomic in nature, the data in this repository is captured at the lowest level of detail possible.
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