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Fact Table Features
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Every fact table represents a business process by capturing measurements that describe it. It is crucial that all relevant measurements be represented, even when some appear redundant. Some facts, however, cannot be aggregated. These nonadditive facts are usually broken down into other facts that can. The level of detail at which the fact table records information is referred to as its grain. It is important to identify the grain of each fact table clearly and avoid situations of mixed grain. Fact tables do not contain rows for every combination of dimension values. Instead, they exhibit a characteristic called sparsity. On occasion, the fact table may host one or more degenerate dimensions. As you will see, these columns may participate in the grain of the fact table.
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The fact table is the engine for business process measurement. It is the locus for storage of the detailed measurements that describe the process. The facts are accompanied by foreign keys that provide dimensional context for each measurement. In some cases, the fact table may also contain dimensions, as discussed shortly. If dimension tables are wide, fact tables are deep. A fact table usually accumulates rows more quickly than the associated dimension tables, often coming to overshadow the dimension tables
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Sample Rows from a Fact Table
when it comes to row count. Fortunately, the rows of fact tables tend to be nice and compact. The foreign keys are usually integers, and the facts are usually integers or floating point decimal numbers. An example is shown in Table 3-1. The compact nature of fact table rows enables the table to accumulate a large number of rows without generating inordinate storage requirements. A list of sample rows from a dimension table would be significantly wider. Not only do dimension tables have more columns but many of those columns contain textual data. In fact, each row would probably be so wide it would be hard to print in this book, even in landscape format. Compared to fact tables, however, dimension tables contain relatively few rows.
Capturing Facts
As the locus for process measurement, the fact table should contain every fact relevant to the process it describes, even if some of the facts can be derived from others. Facts are stored at a specific level of detail but can be rolled up to various levels of dimensionality. This aggregation is made possible by a characteristic called additivity. Some measurements stubbornly refuse to exhibit this trait, but usually they can be broken down into components that do.
Capture All Measurements
In a dimensional design, each fact table describes a business process. It should provide a comprehensive set of relevant measurements, even if some are redundant. The explicit storage of each fact ensures consistent measurement regardless of the tools used to develop queries and reports. Because most facts are numeric, the incremental cost to store an additional column is minimized. An example is evident in the orders star shown in Figure 3-3. The fact table in this star contains the following facts: quantity_ordered order_dollars cost_dollars margin_dollars Notice that margin dollars can be computed by subtracting cost dollars from order dollars. Some designers may be tempted to eliminate the margin dollars, allowing it to be computed within reports, within a view, or through the semantic layer provided by a business intelligence product. Storage of the fact in the fact table, however, allows margin dollars to be computed
Part I
product_key
day_key
salesperson_key
customer_key
quantity_ ordered
order_ dollars
cost_ dollars
PART I Fundamentals
as part of the ETL process. This guarantees consistent computation of margin dollars and consistent representation regardless of the tools being used. Its computation is also preferable to the use of a view, which can hamper efforts to tune the DBMS for optimized query execution. Another common error is to store unit amounts in the fact table rather than extended amounts. As seen earlier, unit amounts can be useful dimensions. They can also be used to compute extended amounts, which are useful facts. For example, the order_facts table in Figure 3-3 contains quantity_ordered and order_dollars. Each of these facts is fully additive, providing full analytic flexibility. If the table had stored unit_price in lieu of order dollars, it would be necessary to compute order_dollars within queries. Explicit storage of the relevant fact enables performance and consistency. NOTE Don t interpret this explanation as a reason to exclude unit amounts from schema designs. In many situations, unit amounts are valuable dimensions for analysis. If there is not a clear dimension table where they can be stored, they may be placed in the fact table as degenerate dimensions, as described later in this chapter.
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