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Multidimensional Models in Business Intelligence: Exploring Cubes and Data Warehouses

Dive into the world of cubes, data warehouses, and multi-dimensional models in BI. Understand ROLAP, MOLAP, and HOLAP concepts, data source views, proactive caching, and more advanced topics. Discover the importance of dimensions, measures, star schemas, and snowflakes in data mart design.

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Multidimensional Models in Business Intelligence: Exploring Cubes and Data Warehouses

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Presentation Transcript

  1. Chapters 4~8

  2. The TWO • Multi-dimensional model • What we meant mostly when referring a cube or a data warehouse • Tabular model • Not common, possible term project

  3. The Three • ROLAP, MOLAP, and HOLAP • ROLAP – relational DB with aggregates, possibly pre-calculated, if not could be slow, if yes, could be too many • MOLAP, generally what we meant by a CUBE • H = R + M • This is very old, in my opinion, concept no one really give much of a consideration

  4. Misc • Data Source Views – chapter 9 • Proactive caching – a fancy name for preprocessed/pre-calculated aggregates • XML definitions • Every object in a cube is defined following the XML format – not really new

  5. Chapter 5 • SSDT (SQL Server Data Tool), used to be the BI development studio, is really the Visual Studio 2010 • Give a demo – end of the story

  6. Chapter 6 • Data Mart – a database with a lot of tables • Known concepts • Dimension • Measure • Star schema • Snowflakes • Follow the steps from 118 to 129 • The use of Cube Wizard (page 120 ~121) could be useful • Do NOT compress your tables

  7. Chapter 7 • Very important • Very useful • Excellent candidate for a term project

  8. Chapter 8 • See the discussions for Chapter 7 • We will go over Chapters 9 and 10 in more detailed discussions than Chapter 8 .

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