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2 Data Warehouse Architectures 19
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Operational Systems Integrated repository of atomic data Normalized format
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A simplified view of W.H. Inmon s architecture: the Corporate Information
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In the Corporate Information Factory architecture, the enterprise data warehouse is not intended to be queried directly by analytic applications, business intelligence tools, or the like. Instead, its purpose is to feed additional data stores dedicated to a variety of analytic systems. The enterprise data warehouse is usually stored in a relational database management system, and Inmon advocates the use of third normal form database design. Surrounding the enterprise data warehouse are numerous other components. Of interest here are the data marts, which appear along the top of the diagram. These are databases that support a departmental view of information. With a subject area focus, each data mart takes information from the enterprise data warehouse and readies it for analysis. As the earlier quotation suggests, Inmon advocates the use of dimensional design for these data marts. The data marts may aggregate data from the atomic representation in the enterprise data warehouse. Note that Inmon reserves the term ETL for the movement of data from the operational systems into the enterprise data warehouse. He describes the movement of information from the enterprise data warehouse into data marts as data delivery. This book will use
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Part I
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PART I Fundamentals
the term ETL more generically, to describe any process that extracts data from one place and stores it somewhere else. The data marts serve as the focus for analytic activities, which may include queries, reports, and a number of other activities. These activities are enabled by a variety of different tools, including some that are commonly referred to as business intelligence tools and reporting tools. This book will collectively refer to these tools as business intelligence tools. Note, though, that Inmon reserves this term for a particular application in the Corporate Information Factory.
Kimball s Dimensional Data Warehouse
Ralph Kimball has made numerous important contributions to the world of data warehousing, and his top two contributions both relate to dimensional design. First, in the 1990s, he was largely responsible for popularizing star schema design. Through his writings, Kimball synthesized and systematized a series of techniques that had been in use as early as the 1960s. He explained how dimensional design provided an understandable and powerful way to develop analytic databases, and he gave us the terminology that is used throughout this book. Second, Kimball developed an enterprise architecture for the data warehouse, built on the concept of dimensional design. Sometimes referred to as the bus architecture, it shares many characteristics of Inmon s Corporate Information Factory. It allows for an integrated repository of atomic data and relies on dimensional design to support analytics. In this book, Kimball s architecture will be referred to as the dimensional data warehouse architecture. To those unfamiliar with Kimball s work, this second contribution often comes as a surprise. Because he is so closely associated with the star schema, he is often assigned blame for shortcomings associated with any implementation that utilizes a star, regardless of its architecture. Other times, the star schema itself is assigned blame. In order to sort things out, it is necessary to take a brief look at Kimball s architecture, which is depicted in Figure 2-2. Again, the diagram is somewhat simplified. In this case, it has been laid out to highlight similarities to Inmon s architecture. Though the diagram in Figure 2-2 appears quite different from that in Figure 2-1, the two architectures actually share many characteristics in common. Like the Corporate Information Factory, this architecture begins by assuming a separation of the operational and analytic systems. As before, operational systems appear on the far left of the diagram. Again, these may incorporate data stores that are relational and nonrelational, and are likely to be numerous. Moving to the right, an ETL process consolidates information from the various operational systems, integrates it, and loads it into a single repository. If that sounds familiar, it should. The Corporate Information Factory has an analogous process. The dimensional data warehouse in the center of Figure 2-2 is the end result of the ETL process. It is an integrated repository for atomic data. Again, that should sound familiar. The same definition was given for Inmon s enterprise data warehouse. It contains a single view of business activities, as drawn from throughout the enterprise. It stores that information in a highly granular, or atomic, format. The dimensional data warehouse differs from the enterprise data warehouse in two important ways. First, it is designed according to the principles of dimensional modeling. It consists of a series of star schemas or cubes, which capture information at the lowest level of
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