Advanced Data Warehouse Modeling Techniques




Solid Q sätter upp en mycket intressant utbildning i avancerad Data Warehouse Modeling med välkända David Mauri och Thomas Kejser den 20:e november i Stockholm.

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In-memory technology is ruling and people keep telling you that a BI solution can be created by just connecting directly to the source of data, and that’s it. Wrong! A Data Warehouse is still needed and vital for the success of a Business Intelligence solution, no matter which technology you’ll use to read, process and present all the gathered data. A Data Warehouse holds the data upon which analysts, decision makers and managers will take their decisions: it must deliver good, high-quality, on-time, certified data. That’s the only way to turn data into information and information into money. In this workshop the Data Warehouse topic will be defined and exposed in detail, starting from the basic theory, and going through well-known models and design patterns, approaches and tools that are needed to build and maintain a Data Warehouse, capable of being flexible in order to support the fast changes needed by today’s business, but also with a well-known and well-defined structure in order to support the ”engineerization” of its development process, making it cost effective.


Thomas Kejser is a former Principal Program Manager of the Microsoft SQL Server CAT team, supporting the toughest Microsoft SQL Server data warehouse projects in the world. He was also one of the teachers of the SSAS Maestros programme. Davide Mauri is a frequent international speaker and co-author of ”Smart Business Intelligence Solution With SQL Server 2008” published by Microsoft Press. He’s developed BI solutions for very large international companies. Davide is also the author of the DTLoggedExec tool.

  • Logical data models: What are they good for?
    • Comparing Inmon and Kimball
  • Demystifying meta data: Meta Models and auto generated ETL
    • Metadata Driven ETL
  • Understanding modern hardware: Co-Located data in SMP and MPP systems
    • How to create a balanced system
  • Why normalization is bad for data warehouses, and what you can do about it
  • Best Practices, Naming Conventions, Lesson Learned
  • Choosing between column and row stores
  • Keys, master data and mapping layers
  • Tracking history in the warehouse
  • Handling changing user requirements
  • High speed data loading – and reloading
  • Unit Testing

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