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Performance is not the only area in which business questions are not created equal. As IT managers are well aware, reports vary in terms of development cost as well. One schema design may deliver sufficient performance for all queries but render a subset of reports very difficult to produce. This complexity can become a concern for two reasons. First, it will be difficult or impossible for end users or analysts to develop these problem reports. They will have to be handled by trained developers or allowed to go unanswered. The former will introduce a lag between question and answer and introduces backlog; the latter will cast the pall of failure over the data warehouse as a whole. Second, complex reports can cost significantly more to develop. While performance may be acceptable, the necessary work involved in producing the report takes longer, leverages advanced or obscure features of the reporting tools, requires additional skill, and is harder to test and modify. For example, a particular business question might require the development of a report that takes query results, parses them into a set of variables, applies layers of conditional logic, and transposes data. Even if it performs well, this kind of report requires more time and skill to produce, test, and maintain. When a large number of important questions are rendered complicated to answer, a derived schema may alleviate these problems. By restructuring data, a derived schema makes answers accessible to users of less skill, and may also reduce report development costs.

So the private key holder is the only one who can decrypt data encrypted by the public key holders, and the public key holders are the only ones who can decrypt data encrypted by the private key holder Figure 10-2 shows how asymmetric cryptography works Several choices can be made regarding which type of encryption to use and how much data should be encrypted in any given communication For example, all the data can be encrypted with a private key so that only the public key holder can decrypt it, or it can be encrypted using a symmetric key known to both sides Another possibility is to append an encrypted piece on to the communication in effect, a signature or seal so that the recipient will know that the sender genuinely sent the data and that the data was not tampered with on the way.

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3. Make some trivial change to the stored procedure. 4. Save the changes (File | Save) in the change file. 5. Right-click the file and choose Check In from the menu. The program prompts

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.

In this case, the sender produces a hash code result by executing an algorithm on the complete message..

FIGURE 10-2

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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