By Teo Lachev
Applied Micrisoft research prone 2005 indicates database directors and builders find out how to construct whole OLAP suggestions with Microsoft research providers 2005 and Microsoft enterprise Intelligence Platform. Database directors will methods to layout and deal with subtle OLAP cubes that supply wealthy info analytics and knowledge mining services.
The ebook provides builders the required history to increase UDM with customized programming common sense, within the kind of MDX expressions, scripts and .NET code. It teaches them how one can enforce quite a lot of reporting purposes that combine with research providers, Reporting prone, and Microsoft Office.
This e-book does not suppose any earlier event with OLAP and Microsoft research providers. it truly is designed as an easy-to-follow consultant the place every one bankruptcy builds upon the former to enforce the elements of the cutting edge Unified Dimensional version (UDM) in a chronological order. New strategies are brought with step by step directions and hands-on demos.
o layout subtle UDM models
o construct ETL strategies with SSIS
o enforce info mining tasks
o increase UDM programmatically with MDX
o expand UDM with SSAS kept procedures
o Create wealthy end-user model
o Optimize research prone garage and processing
o enforce dynamic security
o construct customized OLAP clients
o writer ordinary and ad-hoc reviews with SSRS
o construct Office-based BI purposes and dashboards
o and masses more
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Additional resources for Applied Microsoft Analysis Services 2005: And Microsoft Business Intelligence Platform
In this case, the cube will aggregate the values on the fly. As an UDM designer, you can tell SSAS where the fact data and aggregations will be kept – in the relational database (ROLAP storage), in the cube (MOLAP storage), or both (HOLAP storage). e. when MOLAP storage model is used. In this way, the cube can answer all queries without querying (and impacting the performance) of the underlying data source. On the downside, since the server keeps a copy of dimension and fact data (MOLAP storage option), it has to be updated (processed) when the dimensional structure or source data changes.
Let’s mention a couple of them. Data fidelity lost While dimensions reflect the natural way end-users prefer to analyze data, important dimension characteristics could be lost when transforming OLTP data to fit into the “classic” dimensional model. For example, the Product dimension we’ve discussed has a hierarchy that reflects the natural way end users would prefer to browse the product sales data – by category, subcategory and product name. However, the original Product relational schema may have included columns for product color, size, model, etc.
Up to several gigabytes of data), reporting is typically done directly against the OLTP systems without requiring a designated OLAP server. OLTP data sources As the data volume increases however, OLAP activities may hinder the transactional performance. 2 As data size and complexity increases, OLAP processes are shifted to designated databases and OLAP servers. 42 CHAPTER 2 layer built directly on top of the OLTP database. For lack of a better term, I will dub this approach Transactional OLAP. Note Now that I’ve coined yet another term, you may wonder how Transactional OLAP compares to realtime OLAP I introduced in chapter 1.