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Each of these questions centers on a business process: sales, account management, attendance, payables, return processing. These process-centric questions do not focus on individual activities or transactions. To answer them, it is necessary to look at a group of transactions. Most importantly, each of these questions reveals something about how its respective business process is measured. The study of sales involves the measurement of gross margin. Financial institutions measure account balance. In human resources, they measure number of absences. The finance department measures payables. Purchasing managers watch the return quantities. Without some kind of context, a measurement is meaningless. If you are told sales were $10,000, there is not much you can do with this information. Is that sales of a single product, or many products Does it represent a single transaction, or the company s total sales from conception to date Without some context, the measurement is useless. As with the measurements themselves, context is revealed in business questions or reports. In the preceding questions, for example, gross margin is viewed in the context of product categories and time (the month of January). Sick days are viewed in the context of a department (marketing) and time (last year). Payables are viewed in the context of their status (outstanding) and vendor. These two simple concepts, measurement and context, are the foundation of dimensional design. Every dimensional solution describes a process by capturing what is measured and the context in which the measurements are evaluated.

In concept, SQL performs a subquery over and over again once for each row of the main query. For many subqueries, however, the subquery produces the same results for every row or row group. Here is an example: List the sales offices whose sales are below the average target. SELECT CITY FROM OFFICES WHERE SALES < (SELECT AVG(TARGET) FROM OFFICES) CITY ------Denver Atlanta In this query, it would be silly to perform the subquery five times (once for each office). The average target doesn't change with each office; it's completely independent of the office currently being tested. As a result, SQL can handle the query by first performing the subquery, yielding the average target ($550,000), and then converting the main query into: SELECT CITY FROM OFFICES WHERE SALES < 550000.00 Commercial SQL implementations automatically detect this situation and use this shortcut whenever possible to reduce the amount of processing required by a subquery. However, the shortcut cannot be used if the subquery contains an outer reference, as in this example: List all of the offices whose targets exceed the sum of the quotas of the salespeople who work in them:

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Of course, none of this will help in situations described earlier, in which transactions are not kept online or in which the status measurement does not correspond to any type of transaction. Clearly, an alternative solution is required to support the study of status measurements.

Table 8-1.

As you have seen, a transaction fact table will not suffice for storing a measurement of status. The solution is to design a periodic snapshot fact table, or simply snapshot fact table. The snapshot fact table samples the measurement in question at a predetermined interval. This makes it easy to study the measurement in question, without the need to aggregate a long chain of transaction history. A snapshot fact table design has several properties that distinguish it from a transaction fact table design. Whereas a transaction fact table s grain may be expressed in various ways, the grain of a snapshot fact table is usually declared in dimensional terms. While a transaction fact table is sparse, snapshots are dense. Last, while the facts in a transaction fact table are fully additive, a snapshot model will contain at least one fact that exhibits a property known as semi-additivity.

- 175 -

Useful Stored Procedures for Replication (continued)

A snapshot fact table samples a status measurement at a predetermined interval. This interval, combined with one or more dimensions, will be used to define the grain of the snapshot fact table. Each row will contain a fact that records the status measurement in question. The account snapshot fact table in Figure 11-2 records a balance each day for each account. This declaration of grain involves fewer dimensions than were used for the transaction fact table in Figure 11-1. The day dimension captures the interval at which measurements will be sampled, and the account dimension specifies what will be measured. Account branch and primary account holder are also present, but are not part of the grain. The sole fact, balance, records the status measurement: account balance. With this design, it is now very easy to determine balances at any point in time, for individual accounts and across multiple accounts. Simply constrain the query for the day in question and aggregate the balance fact. This technique can be used to determine the combined balance for a customer across multiple accounts, study account balance by demographic characteristic, look at the balance across all accounts at each branch, and so forth.