barcode vb.net 2010 Transactions, Snapshots, and Accumulating Snapshots 287 in Software

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11 Transactions, Snapshots, and Accumulating Snapshots 287
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One side effect of this requirement generates a notable contrast with other types of fact tables: the dimension table is likely to have more rows than the fact table itself. This should not be viewed as an error; it is in keeping with the analytic requirements. It can pose a tuning challenge for database administrators, who may find that star-join optimization is not the most efficient way to resolve a query involving an accumulating snapshot. Finally, note that an accumulating snapshot does not associate a historically accurate version of the thing being processed with each milestone achieved. It refers to the entity once, and that reference is the most up-to-date version. If the historic context of each processing step is required, this information can be obtained by consulting a transactionbased star, such as the one depicted in Figure 11-5. Together, the two designs can be used to answer a wide variety of business questions. If a transaction design is not required for other purposes, the accumulating snapshot can be supplemented to carry multiple references to the dimension one for each milestone.
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This chapter introduced two new forms of fact table design: the periodic snapshot and accumulating snapshot. Each was motivated by a particular analytic challenge that is not well served by a transaction fact table design, and each has unique characteristics. A transaction fact table tracks events. Its grain usually corresponds to an individual transaction or a summary of transactions. Facts that describe a level, balance, or similar status can sometimes be computed from the transactions that contribute to their value, but not always. When they can be computed, the process is not usually very efficient. A snapshot fact table samples a balance, level, or status measurement at a predefined interval across specified dimensions. Unlike a transaction fact table, a snapshot tends to be very dense. Balance or level measurements in a periodic snapshot are usually semi-additive; it does not make sense to sum them across snapshot periods. In some cases, they may be nonadditive. A snapshot can be embellished with period-begin and -end balances, as well as fully additive facts that summarize the period. They may also include a sum that can be used to compute the average balance or level. A business process that represents a series of steps or statuses may be tracked using a transaction fact table. This model is useful for studying and quantifying the various activities but is notoriously difficult to use when studying the elapsed time spent at one or more stages. If the items being processed can be readily identified, an accumulating snapshot can be designed. It will have one row for each instance of the item undergoing the business process. This row is repeatedly updated as the process unfolds. The accumulating snapshot includes multiple day_keys, each representing the achievement of an important milestone. Facts record the elapsed time associated with each stage. Additional facts may be present that track significant values at the time of each milestone.
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Remember that it is not necessary to make either/or decisions between transaction stars and periodic or accumulating snapshots. Analytic requirements for a particular process may call for a pair of fact tables. In this case, the transaction model should be used as the source for the snapshot or accumulating snapshot.
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The snapshot and accumulating snapshot are common dimensional design techniques, and many additional examples can be found. Whether you are seeking more information or just looking for additional examples, here are some places you will find what you are looking for. Snapshot Fact Tables The examples in this chapter have been chosen to illustrate key design concepts. Although they are set in the financial services industry, these designs should not be interpreted as a complete treatment of the subject area. For a deeper discussion of financial services, consult 9 of The Data Warehouse Toolkit, Second Edition (Wiley, 2002) by Ralph Kimball and Margy Ross. The chapter includes an account snapshot model similar to the one presented here, and incorporates other techniques such as mini-dimensions, householding, multi-valued attributes, and stars for separate account types. This chapter mentions the snapshot model s applicability when studying inventory. For a detailed example, see 3 of The Data Warehouse Toolkit. A snapshot can also be used to study capacity and utilization. An example from the hotel industry appears in 6 of Data Warehouse Design Solutions (Wiley, 1998) by Chris Adamson and Mike Venerable. (The chapter also includes an inventory snapshot design.) Kimball and Ross show how transaction and snapshot can complement each other in an accounting subject area. In 7 of The Data Warehouse Toolkit, they pair a transaction star with a snapshot to model activity in G/L accounts. Snapshots that track the status of other financial processes, such as budgeting and spending, can be found in 8 of Data Warehouse Design Solutions. Kimball and Ross also use a snapshot model to track the status of insurance policy premium payments. You can find it in 15 of The Data Warehouse Toolkit. Accumulating Snapshots This chapter described pairings of a transaction star with either a periodic snapshot or an accumulating snapshot. Kimball and Ross provide a scenario that calls for all three: inventory. As already mentioned, this example can be found in 3 of The Data Warehouse Toolkit. An accumulating snapshot used to track order fulfillment is presented by Kimball and Ross in 5 of The Data Warehouse Toolkit. A similar example appears in 6 of The Data Warehouse Lifecycle Toolkit, Second Edition (Wiley, 2008) by Ralph Kimball, Margy Ross, Warren Thornthwaite, Joy Mundy, and Bob Becker.
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