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design, it is easy to use them to organize data, sort reports, order data, and so forth. Since they are not calculated in queries, it is possible for database administrators to index these columns, providing for efficient query performance.
Codes and Descriptions
In operational systems, it is common for the list of appropriate values in a domain to be described using codes. Elsewhere, a separate table is used to provide the corresponding descriptions. Often called reference values or lookup values, these descriptions may be more useful than the codes themselves. For example, a source table that stores order information might capture the type of customer. Rather than store various values, such as Direct, Indirect, or Other, the table only stores codes such as 001, 002, or 003. A separate reference table maps these codes to the description values. This facilitates maintenance of the appropriate list of values and streamlines storage. From an analytic perspective, both the code and description are useful dimensions. For example, Figure 3-2 shows a type_code of 002 transformed into a pair of attributes in the dimensional design: one for the code and one for the description. Because the dimension table carries both, users are able to filter, access, and organize information in whatever way they see fit.
Flags and Their Values
Columns whose values are Boolean in nature are usually referred to as flags. In an operational system, these values may be stored in several ways. One method uses a column with a Boolean data type. Another method uses an integer, which will contain only the values 0 or 1, or a character, which will contain only the values Y or N. Some systems employ a special case of a code with two possible values: one indicating True and the other indicating False. In Figure 3-2, the source column credit_order_flag contains a Y for credit orders and an N for noncredit orders. In a dimensional design, these flags may be used to filter queries or group facts. By storing a descriptive value for the flag, we make using the flag easier. For example, a report can break up orders into Credit Order and Not Credit Order categories. These descriptors are far more useful than 0/1 or Y/N, and can also be used less ambiguously when defining a query predicate or filter.
Multiple-Part Columns
Operational systems often contain attributes that have multiple parts, each part bearing some sort of significance. Account codes are a common example, made up of parts such as a company identifier, account code, subaccount code, and so forth. In a dimensional design, the entire attribute may be stored, along with additional attributes that isolate its constituent parts. If these subcomponents are codes, they may also be accompanied by corresponding description values. In Figure 3-2, the operational system records a region code in the format XX-YYY. The first part of this code designates a country, and the second part designates a territory within that country. The value 07-701, for example, contains country code 07 and territory code 701, which correspond to the United States and East, respectively. The dimensional design contains the full code, as well as the constituent codes and their corresponding descriptions.
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Dimensions with Numeric Values
While the majority of dimensions contain data that is textual, sometimes dimensions contain numeric data. Given that facts tend to be numeric, this can occasionally lead to confusion. Application of the tests described in 1 will allow you to sort out dimensions from facts. You have already seen examples of dimensions that contain numeric data. In Figure 3-2, for example, numeric content may be found in customer_type_code, country_code, and territory_code. Other common examples of numeric data elements are sizes, telephone numbers, and Zip codes. All of these examples are clearly dimensions. They will be used to provide context for facts, to order data, to control aggregation, or to filter query results. Some numeric attributes are less easy to identify as dimensions. For example, the unit price associated with an order is numeric. If 100 widgets are sold at $10 apiece, is the $10 unit price a fact or a dimension Recall from 1 that if an attribute is commonly aggregated or summarized, it is a fact. If it is used to drive aggregations or summarizations, however, it is a dimension. In the case of a unit price, it is not useful to sum unit prices across multiple orders. On the other hand, it is useful to group orders by unit price, perhaps to answer the question, How many did I sell at $10 each versus $12 each The unit price is, therefore, behaving as a dimension. TIP It is not always clear whether a numeric data element is a fact or a dimension. When in doubt, pay close attention to how it will be used. If the element values are used to filter queries, order data, control aggregation, or drive master detail relationships, it is most likely a dimension. While unit amounts are dimensions, extended amounts are facts. As you will see later in this chapter, multiplying a unit amount by the transaction quantity produces a value that can be aggregated or summarized. The unit amount is a useful dimension, and the extended amount is a useful fact. Both have their place in the dimensional design.
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