Prerequisite: This course assumes a basic understanding of star schema concepts.
Real world data warehouse designs rarely resemble the simple star schemas found in product demos or introductory courses (with only a single fact table, fully additive facts, and several standard dimension tables).
This course takes you beyond fundamental principles of dimensional design, providing an extended set of techniques to address real-world complexity.
The course begins with a brief review of the core concepts of dimensional modeling. These fundamentals are then built upon in four areas: multi-star designs, alternative fact table designs, dimensional intricacy, and scaling.
This comprehensive treatment provides the breadth and depth you will need to meet your data warehouse design challenges—whether you are building a dimensional data warehouse, a Corporate Information Factory, or standalone data marts.
You Will Learn
- 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, and hybrid slow changes
- Techniques to ensure your data warehouse will scale as new subject areas are added
Geared To
- Professionals who need a comprehensive understanding of star schema design, including data warehouse designers, business intelligence developers, report designers, project managers, power users, database administrators, and ETL developers
*Previously titled Dimensional Modeling: Advanced Topics