By Craig Utley
I used to be a section disappointedI needs to first say that Craig's ebook approximately PerformancePoint Server is a brilliant advent publication to tracking and analytics with PerformancePoint Server. for individuals like me which are searching for extra at the making plans facet of the product i used to be upset. just one out of 10 chapters contained information regarding making plans the remainder of the +300 pages is set a number of elements of tracking and analytics. i believe that the assertion from Russ Whitney improvement supervisor tells every little thing concerning the book."An first-class advent to PerformancePoint Server"For humans drawn to making plans they need to pick out one other ebook.
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It is a sturdy publication, however the arithmetic is poorly handled, now not sufficient rigorous as will be anticipated.
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Facts, also called measures, reside in a fact table. The fact table is actually fully normalized by nature, unlike the denormalized dimension tables. Typically, dimension tables have single-field primary keys, often of an integer data type. Each fact represents a unique combination of items for the lowest level of detail for each dimension. As an example, assume three simple dimensions: Time, Product, and Customer. The Time dimension goes to the Day level, the Product dimension goes to the SKU level, and the Customer dimension goes to the Customer level.
The difference between the two is one of scope: marts cover individual business areas, while warehouses cover the entire business. Data marts focus on a particular business function, such as finance, manufacturing, sales and marketing, and so forth. There is a raging, often religious debate about the proper method of building a warehouse: whether to build a number of marts and then roll them up into a warehouse, or build a warehouse and then split out data marts as necessary. This debate is beyond the scope of this book, and the good news is that the decision is unimportant for the purposes of this book.
While entire books are written on the various aspects, this chapter will seek to distill the most important parts of the warehouse building process into an overview that will explain the importance of the decisions made here and how they can affect the delivery of business intelligence information through PerformancePoint Server and ProClarity. It is important to realize that building a data warehouse is not an easy task and many decisions must be made, some of which involve tradeoffs. The overall flow of building a warehouse is generally described as shown in Figure 3-1.