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Schema Scope
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The information in a dimensional schema may serve a variety of groups and purposes. In many cases, analytic requirements will not call for access to all information contained in the schema. When only a subset of the data is called for, a derived schema may be useful. This is most helpful in distributed, departmental, or mobile solutions, where limiting data set size is an advantage. For example, sales data being replicated to regional offices need not include the entire contents of a star. The amount of data propagated to each region can be limited to the region s own transactions. The size of the data set is reduced without sacrificing detail. The same principle can be used to enforce a role-based security requirement. Derived schemas can be produced that are limited to what particular groups are permitted to access. For example, a sliced fact table, as presented later in this chapter, may be used to provide order data for a particular region only.
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Part V
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PART V
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Performance
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Use of Cubes
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While this book uses star schema examples to illustrate dimensional design techniques, any dimensional model can also be implemented as a multidimensional cube. Cubes can be incorporated into a dimensional architecture in two ways, as you learned in 3, Stars and Cubes. One approach calls for the cube as a primary storage mechanism of dimensional data; the other uses cubes as high-performance supplements derived from stars. When a cube serves as the primary storage mechanism for dimensional information, the derivation techniques in this chapter provide the same benefits. Derived cubes are deployed in lieu of derived stars. Like their relational counterparts, derived cubes may be used to improve the performance of queries, reduce the complexity of report development, or limit the scope of the schema. Sometimes cubes are not used for the primary storage of dimensional data because relational structures do a better job storing large volumes of data. Instead, cubes are used as high-performance supplements to the relational data. In this case, the cubes are derived by definition, and may mirror any of the derived schema designs in this chapter. The underlying star schema is relied upon as a data source for a variety of derived cubes, each of which is a compact high-performance data structure, targeted for a specific category of business question.
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Derived Schemas Already Covered
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If you are reading this book cover to cover, you have already encountered several examples of derived schemas. Each offers benefits over the original dimensional schema for specific kinds of analysis. These examples included the snapshot, accumulating snapshot, and core fact tables.
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Snapshots
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Snapshot fact tables are sometimes derived from transaction fact tables, as discussed in 11. A snapshot fact table samples a measurement of status, such as an account balance or inventory level, at a predefined interval. While status can be determined by aggregating individual transactions, doing so can be very inefficient. The snapshot fact table makes this kind of analysis easy. Figuring out your bank account balance on a particular day, for example, should not require aggregating the entire history of transactions dating back to the day you opened it. This is possible but is highly inefficient. If it were necessary to do this for hundreds of accounts at a time, report performance would be very poor. Instead, a daily snapshot of account balances makes short work of figuring out this information. It is constructed from data in the transaction fact table during the load process. There is no need to aggregate years worth of historic transactions during the query process. Snapshot-grained fact tables are not always derived. As noted in 11, some snapshots capture measurements that do not represent the cumulative effect of transactions. Examples include ambient temperature, internal air pressure, and reservoir levels. These measurements can only be captured by sampling real-world conditions at the predetermined interval. In other cases, snapshots may be employed because transactions are too voluminous to keep in the data warehouse. In these cases, the snapshot is not considered a derived schema; instead, it serves as the primary storage mechanism for dimensional data.
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