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Building your first AS solution
Building your first AS solution
Building your first AS solution
Building your first AS solution
Building your first AS solution
Building your first AS solution
Building your first AS solution
Building your first AS solution
Building your first AS solution
Building your first AS solution
Building your first AS solution
Building your first AS solution
Building your first AS solution
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Building your first AS solution

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Presentation for the Bulgarian .NET and BI user group

Presentation for the Bulgarian .NET and BI user group

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  • This topic should be revision for most students. Use it to ensure that all students have a basic understanding of the star schema design used in dimensional data warehouses.Review the articles referenced in the student notes in case students want to better understand how the star join query optimizations in the SQL Server query optimizer work. However, emphasize that the optimizations are automatic and that students do not need to do anything other than use a star schema for the data warehouse tables to benefit from them.
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    • 1. OLAP Services 7.0 • 1998 (Plato) AS 2000 • 2000 (Shiloh) AS 2005 • 2005 (Yukon) AS 2008 • 2008 (Katmai) AS 2012 • 2012 (Denali) • Tabular/MD
    • 2. Product Aggregation of sales value by product, customer, and time • Multidimensional analysis is based on cubes whose core components are measures and dimensions • OLAP cubes have aggregations precalculated during processing
    • 3. Multidimensional models
    • 4. Dimension Attributes Dimension Dimension Attributes Attributes Fact Measures Star schema Dimension Dimension Attributes Snowflake schema Dimension Attributes Attributes
    • 5. Analysis Services Database Cube A Cube B Cube C Dimension Data Source Security Measure groups Measures
    • 6. 2013 integrated

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