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5 Conformed Dimensions 109
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Overlapping dimensions can also conform. Some designers prefer to avoid this situation, since it requires that multiple processes load equivalent dimension columns in the same way. Conformed dimensions are a crucial part of every data warehouse that incorporates the star schema, regardless of architecture. This chapter provided practical advice for use across all implementations, and considerations for each of the major architectures described in 2.
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Conformed dimensions are a central feature of the dimensional data warehouse architecture, produced as part of a strategic design effort. This initial work allows individual implementations to proceed individually, ensuring they will fit together as each comes online. In a Corporate Information Factory, the importance of planning conformance is lessened because of the presence of the enterprise data warehouse. Conformance is still a necessity within data marts, and conformance across data marts can help avoid the need for additional data marts to cross subject areas. Stand-alone data marts do not conform. The associated risks can be partially mitigated by planning for conformance of a few key dimensions with known importance across the enterprise. Stand-alone data marts may be retrofitted to work with existing conformed dimensions, but this process is not trivial.
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Further Reading
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This chapter observed that dimensional models often develop chains of stars that represent interrelated business processes. Other examples are worth reading about: Ralph Kimball and Margy Ross discuss the concept of a value chain in a retail context in 3 of The Data Warehouse Toolkit, Second Edition (Wiley, 2002). Mike Venerable and I describe the chain of relationships between budgets, commitments, and spending in 8 of Data Warehouse Design Solutions (Wiley, 1997). Discussion of conformed dimensions can be found in any book that deals with dimensional design. Of particular note are the following treatments: Kimball and Ross present conformed dimensions as the key to bus architecture in 3 of The Data Warehouse Toolkit. Conformed rollups can be considered a form of aggregation since they summarize a base dimension. In 3 of Mastering Data Warehouse Aggregates (Wiley, 2006), I provide tips for aggregate dimension design and ETL requirements.
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Conforming dimensions are best illustrated through matrices, since the number of criss-crossing relationships can easily clutter a table diagram. Matrices can describe conformance within a data mart or across data marts.
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The last part of this chapter touched on the process of planning data warehouse implementation projects around conformed dimensions. More detailed coverage can be found elsewhere. Depending on your architecture, you might want to consult one of these books: For the dimensional data warehouse, a more detailed discussion is provided in The Data Warehouse Lifecycle Toolkit, Second Edition (Ralph Kimball, Margy Ross, Warren Thornthwaite, Joy Mundy, and Bob Becker; Wiley, 2008.) This book deals with the entire data warehouse life cycle. Conformance is specifically addressed in 7. A process tuned to the Corporate Information Factory architecture is spelled out in Mastering Data Warehouse Design (Claudia Imhoff, Nicholas Galemmo, and Jonathan Geiger; Wiley, 2003). Here, conformed dimensions in data marts are a natural extension of the major pillars of each subject area in the atomic data warehouse, which is developed from operational sources through an eight-step process described in 4 of that book. Tools for planning implementations around conformed dimensions and designing and documenting conformance are provided in 18, How to Design and Document a Dimensional Model.
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ChAPTeR 6
More on Dimension Tables
PART
Dimension Design
ChAPTeR 7
Hierarchies and Snowflakes
ChAPTeR 8
More Slow Change Techniques
ChAPTeR 9
Multi-Valued Attributes and Bridges
ChAPTeR 10
Recursive Hierarchies and Bridges
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32 6
ChAPTeR
More on Dimension Tables
Dimension tables are the foundation of powerful analytics. You have already learned much about them in previous chapters. These fundamentals, however, do not address a variety of real-world concerns that you are likely to encounter. The next several chapters move beyond the basics, covering a progression of advanced dimension design topics. Mastery of these topics will prepare you to confront real-world complexity and respond to it with simple and powerful solutions. If you are not reading this book from cover to cover, you may be drawn to this chapter because you have a question involving dimensions. Some of what you are looking for may have appeared in prior chapters. Here is a quick recap of what has already been covered: 1 introduced the dimension as a fundamental part of the dimensional model, providing a context for facts and enabling rich and powerful reporting. Techniques were provided to identify dimensions in reports and the spoken word, and the star schema was introduced. 3 described the dimension table in detail, from its rich set of attributes to its use of surrogate keys to the ways in which type 1 and type 2 slowly changing dimensions represent history. 5 explained conformed dimensions, which enable cross-process analysis through a procedure called drilling across. The key to scaling across subject areas, conformed dimensions allow fact tables to work together to produce powerful reports. This chapter builds on these fundamentals to address a handful of the more complicated issues surrounding dimension design. It is divided into five sections, covering the following topics: Grouping Dimensions into Tables describes how to identify when dimensions belong in the same table and when they should be placed in separate tables.
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