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the product being ordered, the customer who placed the order, and the salesperson who took the order. Degenerate dimensions identify the particular order line, and the junk dimension order_info specifies miscellaneous characteristics of the order. Each row in this fact table refers to a specific day, product, customer, salesperson, and order. For example, a fact table row may record the fact that on January 1, 2008 (a day), Hal Smith (a salesperson) took an order for 100 black ballpoint pens (a product) from ABC Stationery Emporium (a customer) as part of order number 299113. The fact table row records a relationship among these instances of day, salesperson, product, customer, and order. They are related to one another in the context of this particular order. Each of these dimension instances ABC Stationery, Hal Smith, January 1, black ballpoint pens may be related in other ways as well. ABC Stationery Emporium may have ordered other things from Hal Smith, perhaps on the same order or perhaps on completely different days. All of these relationships are made explicit by recording additional rows in the fact table, using the appropriate foreign keys. Each of these is a separate relationship in the context of an order. These dimensions can also be related in other contexts. A customer and salesperson, for example, may also become related when a proposal is presented, a product is returned, and so forth. If customer and salesperson can be related in different contexts, they belong in separate dimension tables. Fact tables will provide the different contexts. Those familiar with entity-relationship modeling are doubtless familiar with this type of explicit relationship. Every fact table is an example of what ER modelers refer to as an intersect table. It resolves a potential many-to-many relationship between each of the associated tables. Another type of relationship is implied in dimensional models, one that does not involve primary key / foreign key associations.
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Implicit Relationships Describe Affinities
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Unlike an entity-relationship model, a dimensional model also includes relationships that are not made explicit through joins. Although dimensional modelers do not think about their models in these terms, this distinction can be a source of confusion for ER modelers who are new to star schema design. Relationships between dimension attributes can be implied through their coexistence in a table. These relationships tend to exist only in a single context, representing a natural affinity rather than one based on process activities. The relationships among attributes in a dimension table may change over time but tend to be less volatile than those of the explicit variety. When implicit relationships do change, their history can be preserved through a type 2 slow change response. The orders star from Figure 6-1 contains many examples of implicit relationships. Within the product table, for example, are dimension attributes called product and brand. Since more than one product may share the same brand, an ER model would isolate these attributes in separate tables, relating them via a primary key / foreign key relationship. This approach makes sense in the context of an operational system, which must often support a high volume of concurrent transactions inserting, updating, and deleting data. As you learned in 1, dimensional models are not intended for an operational profile. Instead, they are optimized to support queries that potentially aggregate large volumes of data.
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6 More on Dimension Tables 117
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In this context, there is no need to separate brand from product. To do so would potentially impact the performance of queries involving large volumes of data by requiring additional join processing. NoTe In some situations, dimensional modelers use primary key / foreign key associations to make this kind of relationship explicit. This typically results in a variant of the star schema known as a snowflake, which will be discussed in 7, Hierarchies and Snowflakes. Unlike the relationship between a customer and salesperson, the relationship between a product and brand does not take on multiple contexts. Products and brands are related in only one way: membership in a brand. It is a natural affinity that does not depend on the execution of business activities. At a given point in time, a particular product has one associated brand. This relationship does not depend on a sale, the manufacturing process, or other significant processes tracked by the business. The relationship is not necessarily constant. It may change over time, and when it does, the change history can be tracked. If the brand designation of a particular product changes, for example, history can be preserved through a type 2 slow change. A new row is added to the dimension table for the product, and this new row contains the new brand designation. NoTe Perhaps a small audience is interested in tracking a business process that assigns brands to products. While this analytic requirement may suggest a fact table, the relatively small amount of activity argues for a different approach. 8, More Slow Change Techniques, provides a potential solution in the time-stamped dimension.
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