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PART I Fundamentals
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Enterprise Level Integrated Repository of Atomic Data Corporate Information Factory Dimensional Data Warehouse Stand-Alone Data Marts Format Direct Access Data Marts
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Figure 2-6 Characteristics of each architecture
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department or business process. Their profiles and capabilities differ significantly. These are summarized under the subject area heading in Figure 2-6. For Inmon, the data mart is a separate physical set of tables built for departmental use. It may aggregate detailed data and be built to suit the specific viewpoint of a department or group. In these respects, it bears some similarities to the stand-alone data mart; however, the data mart in a Corporate Information Factory draws its information from the enterprise repository. Its content is, therefore, consistent with an enterprise view of information. The same cannot be guaranteed for the stand-alone data mart. For Kimball, the data mart is not required to be a separate physical data store. Instead, it can be a logical construct a subset of the warehouse tables. Optionally, separate data mart tables may be built. When this is done, they will draw from the integrated repository. The data marts are consistent with an enterprise view of information, either because they embody that view or because they draw from it.
The Common Element
Although they have significant differences, each of these architectures has a place for dimensional design. Kimball s dimensional data warehouse emphasizes it most strongly, relying on dimensional data structures to serve both enterprise and departmental needs. Inmon relies on the dimensional model to provide a departmental solution in the context of an enterprise solution. The stand-alone data mart uses dimensional design without any enterprise context. Regardless of how dimensional design is used, the techniques are the same. The concepts described in this book apply in each of these architectures. You can use them to build a full-blown dimensional data warehouse, in keeping with Ralph Kimball s approach. You can use them to design data marts that serve as departmental spokes surrounding a central data warehouse, as in Bill Inmon s architecture, or you can put them to use in support of a single subject area, as a stand-alone data mart. This is not to say that architecture does not influence dimensional design. While the techniques are universal, some best practices will vary based on architectural context. This will be most evident in 3, where you will learn that in a dimensional data warehouse,
2 Data Warehouse Architectures 27
fact table grain must be set at the lowest possible level of detail. This guideline can benefit the other architectures as well but is not required. Similarly, 5 will highlight the strategic emphasis on dimensional conformance in a dimensional data warehouse. Conformance is the central feature in this architecture, enabling the compatibility of information across subject areas. Conformance benefits the other architectures as well but is not the key to providing an integrated view of enterprise data. Having looked at the different ways dimensional design is used, it should now be clear that the following common misconceptions are false: Inmon is anti star schema. Kimball does not think an enterprise focus is necessary. Data marts are islands of information. Dimensional design is for aggregated data. Star schemas are stovepipes. Some of these misconceptions result from superficial or sensationalized treatment of Inmon vs. Kimball. Others stem from the failure to separate dimensional design, or the concept of a data mart, from a particular approach.
Terms Used in This Book
These three architectures use the same terms to describe different things. While this book does not make any assumptions about your architecture, it is not possible to avoid using terms like data warehouse or data mart. When you encounter these terms in this book, here is what they mean: Data warehouse will describe any solution that contains an analytic database, including stand-alone data marts. This term will not be meant to imply a central or integrated repository. Enterprise data warehouse will be used to refer to the central repository of the Corporate Information Factory. Use of this term is not meant to imply that other data warehouse architectures lack an enterprise focus. Data mart will refer to a subject area in any architecture. Use of this term is not meant to imply departmental focus or the lack of an enterprise context. ETL will refer to any activity that moves information between structured data stores. The use of this term in the generic sense is not intended as a slight to the Corporate Information Factory, where it has a more specific definition. Source system will refer to the computer system(s) from which a star schema gets its data. In the case of a data mart in a Corporate Information Factory, the source system is the enterprise data warehouse. In the case of a star schema in a dimensional data warehouse or stand-alone data mart, the source system is an operational system. Business Intelligence will be used to describe any software product used to create reports and other information products for end users. It is not meant to imply a separate analytic data store, nor the use of any particular form of packaged software.
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