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CHAPTER
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Hierarchies and Snowflakes
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It is possible to describe a dimension table as a series of parent-child relationships among groups of attributes. Days make up months, months fall into quarters, and quarters fall into years, for example. This chapter explores these attribute hierarchies and their implications for the design and use of dimensional databases. The chapter begins by developing a basic concept of what it means to drill into data. The attribute hierarchy is then introduced, and its use as a path for drilling up and down is explored. Drilling within an attribute hierarchy turns out to be one out of many ways to drill into data; several other ways to drill are described in detail. Although a hierarchical view of drilling limits analytic possibilities, many software tools tie their drilling features to the concept of hierarchies. When such a product is being used, it is useful to understand the attribute hierarchies in each dimension. This information may also be helpful in planning conformed dimensions, designing cubes or aggregate tables, or configuring software products that generate them. As with drilling, however, be advised that hierarchies are only one way to summarize information; alternative possibilities exist. When an attribute hierarchy is instantiated as a series of physical tables, rather than as a single dimension table, the result is a variation of the star schema known as a snowflake. You may be particularly tempted to follow this design approach if you have a background in entity-relationship (ER) modeling. Developed for use in an operational setting, this approach offers benefits that do not apply in an analytic setting. Unless a specific software product in your architecture is optimized for the snowflake, it will offer no advantages. On rare occasions, a limited form of snowflaking is employed to help resolve unmanageable row length or to ensure consistent representation of repeating attributes. Attributes are removed from a dimension table and relocated to a table called an outrigger. The outrigger and dimension tables share a parent child or master detail relationship. Because of the potential drawbacks to snowflaking, it is prudent to consider all other options before taking this step.
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PART III
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Dimension Design
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Drilling
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We often talk about analysis as the process of drilling into data. When you look at a report, for example, you might decide you want to know more. You choose to drill down. A summarized view is replaced with a more detailed view. You may choose to drill deeper or to go back up and look at things a different way. This interactive exploration of facts characterizes much of the interaction users have with the data warehouse or data mart.
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The Concept of Drilling
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The word drill connotes digging deeper into something. In a dimensional context, that something is a fact. A generic concept of drilling is expressed simply as the addition of dimensional detail. Figure 7-1 illustrates this concept. A basic report on the left-hand side of the figure shows a fact, order dollars, broken out by the dimension attribute called category. The dimension attribute month is added to this view, resulting in the report on the right, where order dollars for each category are broken down by month. In this example, drilling could also have been achieved by replacing month with any other dimension deemed useful. Order dollars by category might be broken down by salesperson, product, customer, or industry. Each provides a different way to further understand order_dollars by category, providing a more detailed picture of the fact. This basic concept of drilling is sometimes referred to as drilling down, in order to distinguish it from the converse activity drilling up, wherein dimensional detail is removed from a report. Removal of month from the report on the right side of Figure 7-1, for example, would be an example of drilling up. The term is an oxymoron, since drilling up in a report is the opposite of moving deeper into something. A detailed view of a fact is replaced with one that is more summarized. Perhaps the word undrill would be a better choice, but drill up sounds better.
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2 Order Dollars 88,561.00 18,590.23 143,424.71 20,997.93 Tape Mailers Order Dollars 23,520.20 33,120.03 31,920.77 8,544.00 7,763.01 2,283.22 49,920.01 29,088.37 64,416.33 14,997.84 2,880.01 3,120.08
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