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You could stop after creating the data warehouse. Some do. Analysis Services, or any OLAP technology, requires a new skill set to develop and maintain cubes. But a cube solution vastly improves the analytical and usability factor in the resulting solution.
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Users no longer have to know how to write T-SQL or be constrained by static reports. There are also a number of tools that consume OLAP data sources Excel (shown in figure 3), PerformancePoint, Reporting Services, and Panorama, just to name a few. And the query response speed can go from many minutes (or hours) for a SQL query to seconds from a cube. The power of online analysis is the ability to quickly ask a question, get an answer, and then ask another question based on the first result. Analysis Services cubes are specifically designed to retrieve thousands or millions of data points and aggregate them quickly. Some of this performance is accomplished through aggregations, which precalculate the data at predefined intersections of dimensions. Think of it as building several indexed views to provide summarized information in SQL Server. The difference in query time often differs in orders of magnitude. There is a common misconception regarding where cube data is stored. Cubes have their own separate data store in addition to the data warehouse. This can be greatly minimized using ROLAP (relational OLAP) partitions, but this is universally considered a bad idea except for small real-time partitions, due to performance. MOLAP (multidimensional OLAP) is the preferred storage option, which results in a redundant data store that is specifically tuned for OLAP query performance. Cube processing is the activity that loads data into the cube from the data source.
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Resist cutting corners in regard to storing data in the data warehouse. It might be tempting to prune data from the data warehouse once it is contained in the cube, but this would remove your ability to reprocess the associated cube partitions, and that data would eventually be lost, because cubes must be reprocessed if associated dimension structures change. Again, the relational DBA approach is all about economizing on disk usage, but that mindset is at odds with the data warehouse implementation.
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Microsoft provides all the tools required to implement a business intelligence solution, from the relational data warehouse store to the front-end analytics. Microsoft s initial approach was to provide only the back-end tools, up to the delivery of OLAP cubes. They relied upon partners, such as Proclarity and Panorama, to deliver the front-end functionality. Over the last few years, Microsoft has completed the stack with the development of Business Scorecard Manager and the acquisition of Proclarity. Both of these products are now released in a single product called PerformancePoint. The Microsoft BI stack is represented in figure 4.
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This chapter was intended to give you a basic introduction to business intelligence and to explain why it differs so dramatically from the traditional OLTP approach. From here, my best recommendation is to start the way I started, with a book I cannot recommend enough: The Data Warehouse Lifecycle Toolkit by Ralph Kimball and others. One of the things that I like about it is that it not only explains the what, but it also explains the why. I occasionally deviate from the approaches recommended in the book, but only after reading the why to be sure there was nothing I was missing. This book is not technical; it is more specific to the methodology, which is what is most
The Microsoft BI stack
Summary
lacking in technicians coming from an OLTP environment. It does contain some great information on dimensional modeling, including techniques for handling common scenarios that arise when trying to represent business data in a dimensional model. It is difficult to make a universal recommendation about how to get your feet wet. One option is to develop your own cube to represent a solution to a problem. One of my favorite projects was to build a cube to analyze how disk space was being used on my computer. I have a difficult time identifying files that are duplicated in several folders, and building such a cube helped me identify where most of my space was going (I freed up about 30 percent of my disk space based on this analysis!). Another implementation could analyze server resource consumption. In fact, such a solution is available for download as a PerformancePoint sample (see Scorecards and Dashboards for your IT Department at http:/ /www.microsoft.com/bi/department/department. aspx id=tab1), so you can validate your approach. If there is a data warehouse project in development or already implemented at your company, you may be able to find a way to become a team member and learn from other practitioners with more experience. And there are courses available from the major training vendors to get you started.
Summary
I hope you have gained some insight into the various aspects of business intelligence and learned how it differs from legacy OLTP applications. This should give you the fundamentals, so you can decide if this is something you want to pursue. I have found business intelligence projects to be some of the most rewarding in my professional career. Historically, IT projects have typically been about getting information entered into computer systems, so there is no greater reward than to see the excitement on a business user s face when they see how easy it can be to make some actionable knowledge out of years of stored data.
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