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Figure 7-9 Outrigger and cascading slow change
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This chapter explored some of the intricacies that hide within dimension tables. Many tools link the concept of attribute hierarchies with drilling, so you may be forced to document them. Usually it is unnecessary to instantiate attribute relationships in physical tables, unless you have a tool that requires it. On rare occasions, it may be useful to design an outrigger,
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7 Hierarchies and Snowflakes 169
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which takes advantage of these relationships to ensure consistency. Key takeaways from this chapter include: Drilling is the activity of adding dimensional detail to a fact. It does not require the presence of a hierarchy, although many software tools do. An attribute hierarchy describes parent child relationships between groups of attributes within a dimension. Although some reporting tools define drilling as moving up and down a hierarchy, there are other ways to add and remove detail from a report. Some of these methods may add detail even as they move up a hierarchy. If you are working with a business intelligence tool that links drilling to hierarchies, it may be useful to document attribute hierarchies. This may also help with the planning of conformed dimensions, cubes, and aggregates. When relationships between attributes in a dimension are expressed explicitly, the result is called a snowflake schema.
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Outriggers can be used sparingly when attributes repeat within a table or appear in multiple tables. This limited form of snowflaking guarantees a single consistent ETL process. Outriggers may also be helpful if dimension row length is causing technical issues, and other options have been exhausted. As mentioned in this chapter, there is still more to be said about situations in which a single dimension table is replaced by two or more tables. 9 describes how bridge tables resolve situations in which dimension tables or attributes must repeat an indeterminate number of times. 10 discusses how bridge tables can be leveraged to summarize facts across a recursive hierarchy.
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Perhaps because the reality of drilling is so tool-specific, not a lot is written about what it means to drill in a dimensional model. You will also not find a lot written about attribute hierarchies in texts on dimensional modeling since they are limited ways to view drilling or summarization, and because they are not a physical feature of the database schema. A lot of what you will find written about hierarchies deals with what I call instance hierarchies, which will be covered in 10. Drilling Kimball and Ross define drilling down as the process of adding row headers to a report, regardless of what attribute was used to provide them. They don t spend a lot of time on this topic, which makes sense given how many ways the term can be used, but you can read their description in 2 of Kimball and Ross s The Data Warehouse Toolkit, Second Edition (Wiley, 2002).
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Snowflaking makes sense only if it maximizes the capabilities of your reporting tool or DBMS. Altering your design for a specific tool, however, limits your ability to use other tools in the future.
PART III
Dimension Design
Attribute Hierarchies This chapter touched on the notion that attribute hierarchies may be of use to tools that automate the creation and maintenance of cubes or aggregate tables. You can read more about different ways tools do this in 4 of Mastering Data Warehouse Aggregates (Wiley, 2006), where I provide examples of the automatic generation of brand-level aggregates in a product dimension. Figure 7-3 provided an example of a dimension that included more than one hierarchy, and this chapter mentioned that these two hierarchies can also be diagrammed separately. This is not hard to envision, but, if necessary, you can find an illustration of such an alternative in 3 of Mastering Data Warehouse Aggregates. For an alternative way to graphically depict multiple hierarchies within a dimension, see 7 of A Manager s Guide to Data Warehousing by Laura Reeves (Wiley, 2009). Reeves defines hierarchies as relative cardinalities among attributes, and provides a mechanism for diagramming them. Snowflakes This chapter advised against the normalization of dimension tables unless your software products require it. For many people, the desire to do so can be quite strong. If you are still tempted to snowflake or are interested in a more detailed argument against the practice, Kimball and Ross provide a list of five reasons not to snowflake in 2 of The Data Warehouse Toolkit, Second Edition. Saving storage space is often advanced as an argument in favor of snowflaking. This argument is discussed in 6 of The Data Warehouse Lifecycle Toolkit, Second Edition (Wiley, 2008) by Kimball, Ross, Thornthwaite, Mundy, and Becker. Using a specific example, the authors compute the space saved and show it is a very small fraction (measured in thousandths of a percent) of the overall space consumed by a star. Separately, Kimball and Ross acknowledge that space savings can be more significant in limited situations (see next). Outriggers When a dimension has more than one relationship to the same outrigger, as in Figure 7-7, even the most careful developer can easily make a mistake. The result might be using the location_name where someone reports, rather than the location_name where they work. In The Data Warehouse Toolkit, Kimball and Ross recommend creating views for each role and renaming the attributes to be specific to the role. You can read their example, which involves a date outrigger on a store dimension, in 2 of The Data Warehouse Toolkit. This book has pointed out several times that space savings in a dimension usually saves very little in cost, while adding a lot in terms of complexity. Kimball and Ross point out that the benefits may be greater for very low cardinality attributes. You can read their example in 6 of The Data Warehouse Toolkit. Normalization For detailed information on the principles of normalization, see Part III of An Introduction to Database Systems, Eighth Edition (Addison-Wesley, 2003) by Chris Date.
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