Dimensional Data Modeling Skills – A Deep Dive Training

  • Learn via: Classroom / Virtual Classroom / Online
  • Duration: 3 Days
  • Download PDF
  • We can host this training at your preferred location. Contact us!

Dimensional data is a core component of modern business intelligence and analytics. It is widely accepted as a proven and practical way to design and implement business measures and metrics. Dimensionally organized data provides a strong foundation for performance dashboards and scorecards, and offers a more effective and adaptable solution to business analytics needs than can be achieved with relational data structures.

Over three days of in-depth, interactive training, the Dimensional Data Modeling Skills workshop will provide your team with comprehensive coverage of dimensional modeling principles, processes, and deliverables, from the author of Star Schema: The Complete Reference. Starting from the basics, learn a complete set of best practices—from multiple fact table designs, to bridge tables, to advanced slow change processing. Build the skills to match these techniques to real-world business complexity and apply them to solve real modeling problems. Learn how to fit dimensional modeling into agile development frameworks. You’ll receive templates to capture requirements and designs and complete the class with a robust understanding of dimensional data modeling pragmatics.

There are no prerequisites for this course.

  • Professionals who need a comprehensive understanding of dimensional data modeling and star-schema design:
  • BI program managers
  • Business analysts
  • Data architects and modelers
  • BI architects and developers
  • Data integration architects and developers
  • Project managers
  • Database administrators
  • “Power users” and business subject matter experts

  • How to build a logical dimensional data model
  • How to translate a logical dimensional model into a star-schema design
  • Why most subject areas require multiple fact tables, and how to identify them
  • When to use alternatives to the basic transaction fact table, including periodic snapshots, accumulating snapshots, and type-specific stars
  • How to cope with dimensional intricacy using techniques such as bridge tables, mini-dimensions, time-stamped dimensions, hybrid slow changes, and other slow change options
  • Techniques to ensure your data warehouse will scale as new subject areas are added
  • How design fits into development methods, who should be involved in design activities, and what tasks and outputs should be incorporated

Contact us for more detail about our trainings and for all other enquiries!