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X IN (A, B, C) is completely equivalent to: (X = A) OR (X = B) OR (X = C) However, the IN test offers a much more efficient way of expressing the search condition, especially if the set contains more than a few values. The ANSI/ISO SQL standard doesn't specify a maximum limit to the number of items that can appear in the value list, and most commercial implementations do not state an explicit upper limit either. For portability reasons, it's generally a good idea to avoid lists with only a single item, such as this one: CITY IN ('New York') and replace them with a simple comparison test: CITY = 'New York'

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).

5:

Pattern Matching Test (LIKE)

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.

There are several ways that larger networks can be formed from many little ones. It is beyond the scope of this book to discuss the issues associated with network design, but routing and switching technologies can make a big difference in how backups are performed. For example, if all of your company s servers are located on an isolated subnet that is separated from others by a switch or router, performing backups of all servers can be performed without causing excessive traffic for other machines. Figure 5-3 shows an example of a network connected by a router. Network issues are especially important when wide area networks (WANs) are used. In contrast to LANs, WANs are not entirely controlled by an organization; they are at least partially dependent on leased, public lines. An example of a WAN would be several remote offices connected by analog, ISDN, frame-relay, or T-1 lines. (These types of lines provide much lower bandwidth than typical LAN connections; consequently, the amount of data that can be transferred over a WAN is severely limited.)

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.