barcode vb.net 2010 PART IV in Software

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PART IV
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Fact Table Design
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but others will require two or more. This chapter arms you with an understanding of the capabilities of each, so you can choose the right design for every situation.
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Transaction Fact Tables
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The fact tables presented in previous chapters share an important characteristic: they all track events. Events measured by these fact tables have included the booking of an order, the shipment of a product, and a payment on a policy. These examples all represent a type of fact table called the transaction-grained fact table, or simply transaction fact table. Other kinds of fact tables exist, and their properties differ from transaction fact tables in several ways. Before looking at these, it will be useful to review some notable properties of transaction fact tables.
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Describing events
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Transaction fact tables capture details about events or activities. By storing facts and associated dimensional detail, they allow activities to be studied individually and in aggregate. The facts measure the activities: the margin on an order, the quantity shipped, or the dollar value of a payment. Each value recorded in the fact table describes the specific event represented by the row, and nothing else. The word transaction has a formality to it and connotes the exchange of goods, services, or money. In fact, any kind of event can be considered a transaction. Examples you will encounter later in this book include tracking phone calls from customers and the logging of student attendance. Although no money passes hands, these activities can be considered transactions.
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Properties of Transaction Fact Tables
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You have already learned a great deal about fact tables, from the use of surrogate keys to the need to model separate fact tables for separate processes. Three important fact table characteristics are worth revisiting: grain, sparsity, and additivity. In these three areas, the properties of transaction fact tables contrast with those introduced later in this chapter.
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Grain of Transaction Fact Tables
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Declaration of grain is a crucial step in the design of every star. As you learned in 3, there are two ways to declare the grain of a transaction fact table. The grain may be defined by referencing an actual transaction identifier, such as an order line, or the grain may be specified in purely dimensional terms, as in orders by day, customer, product, and salesperson. Both of these methods identify the level of detail represented by a fact table row. Although the word transaction appears in its name, a transaction fact table s grain is not always the individual transaction. Many real-world transaction fact tables summarize activities, either because detail is available elsewhere or because the transaction volume is too large. The first fact table to appear in this book, in Figure 1-5, aggregated orders by day, salesperson, customer, and product. If the same salesperson booked two orders from the same customer for the same product on the same day, then both would have been combined in a single row in the fact table. Each row of the fact table describes specific events, though not individual events.
11 Transactions, Snapshots, and Accumulating Snapshots 261
Transaction fact tables must have clearly defined grains, but this does not imply a mandatory relationship to all dimensions. For dimensions that do not participate in the grain of the fact table, an optional relationship is possible. In retail sales, for example, some transactions have a salesperson, but not all do. To avoid recording NULL-valued salesperson keys in the fact table, 6 advised creating a special row in the salesperson dimension to represent the house or not applicable.
Transaction Fact Tables Are Sparse
Second, recall that transaction fact tables are sparse. As you learned in 1, this characteristic follows logically from the statement of grain. Rows are recorded only for activities that take place, not for every possible combination of dimension values. For example, the simple orders star from Figure 1-5 did not record rows each day for every combination of salesperson, product, and customer. This would have led to excessive growth and cluttered up reports with numerous extraneous rows where all the facts had values of zero. Instead, rows were only recorded when orders took place.
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