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For more information on the design of operational systems, there is no finer reference than Chris Date s An Introduction to Database Systems, Eighth Edition (Addison Wesley, 2003). This book fully explains the principles of normalization used to support transaction processing in a relational database management system. A wealth of information is available on the differences between operational and analytic systems. Two good places to start are 1 of Ralph Kimball and Margy Ross s The Data Warehouse Toolkit, Second Edition (Wiley, 2002) and Inmon, Imhoff, and Sousa s discussion in The Corporate Information Factory, Second Edition (Wiley, 2000). For more information on separating facts from dimensions, you can consult any book on dimensional design. Data Warehouse Design Solutions (Adamson and Venerable; Wiley, 1995) and Mastering Data Warehouse Aggregates (Adamson; Wiley, 2006) both cover the topic in their opening chapters, as does Kimball and Ross s The Data Warehouse Toolkit. These books also cover the prototypical query pattern for a star schema; the browse query is discussed in The Data Warehouse Toolkit.

Recognize the Effect on Each of the Following Characteristics of Two-tier, Three-tier and Multi-tier Architectures: Scalability Maintainability, Reliability, Availability, Extensibility, Performance, Manageability, and Security.

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3 1 D is correct Successful software architecture deals with addressing the nonfunctional service level requirements of a system The design process takes all functional business requirements into account Security is considered a nonfunctional requirement and specific business rules, such as the one described for the checking account, are considered functional requirements Choice D is the only choice that accurately describes this A, B, C, and E are not true Choice A is incorrect because the functional and nonfunctional requirements are switched Choice B is incorrect because only one of them is a functional requirement Choice C is incorrect because, as just described , one of them is a functional requirement and the other, a nonfunctional requirement Finally, Choice E is incorrect because business analysis may start with use cases 3 2 D is correct.

There is a wide range of opinion regarding the optimal data warehouse architecture Opinions are sometimes so strongly held that a colleague of mine often refers to a religious war in data warehousing That may overstate things, but everyone will agree to this: data warehouse architectures vary widely One of the ways in which data warehouse architectures diverge is in their use of dimensional design Some architectures place a heavier emphasis on the star schema, while others use it in a limited capacity The principles of dimensional design are the same, wherever they are put to use This book is concerned with these principles With a diversity of architectures, however, comes confusion The same terms are used to describe different things Different terms are used to describe the same thing Characteristics of one approach are misinterpreted to apply in other situations.

The nonfunctional service level requirements discussed are performance (I: The system needs to respond within 5 seconds); availability (II: The system needs to have a 999 percent uptime); scalability (III: An additional 200,000 subscribers will be added); and security (IV: HTTPS is to be used) Hence, choice D is correct A, B, C, and E are incorrect There is no mention of extensibility (ability to easily add or extend functionality) and manageability (ability to monitor the health of the system) Hence, choice A is incorrect Specification, elaboration, construction, transition, documentation, and use cases are not nonfunctional service level requirements Hence, choices B and C are incorrect While scalability and reliability may be related (Will the system perform as reliably when more users operate on it ), there is no mention of reliability in the question Hence, choice E is incorrect 3 3 D is correct.

allows you to drag and drop relation tables to link them and then build XSD schema out of them. To use it:

In order to understand dimensional design, it is important to clear up this confusion To do so requires a brief look at data warehouse architecture This chapter groups data warehouse architecture into three categories The first two are often called enterprise data warehouse architectures, and are closely associated with W H Inmon and Ralph Kimball, respectively The third does not have a well-known figurehead but is equally common While these architectures differ in fundamental ways, there is a place for the star schema in each of them By understanding these approaches, we can avoid misunderstandings in terminology and develop a clear understanding of the capability of the star schema If you are looking for an answer to the question, What is the best data warehouse architecture you will not find it here There is no discussion of pros and cons Nor will you find comprehensive specifications for each architecture.

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