Stars, Flakes, Vaults and the sins of Denormalization

 Instructor, Stephen Brobst

Providing both performance and flexibility are often seen as contradictory goals in designing large scale data warehouse implementations. In this course we will discuss techniques for denormalization and provide a framework for understanding the performance and flexibility implications of various design options.

We will examine a variety of logical and physical design approaches and evaluate the tradeoffs between them. Specific recommendations are made for guiding the translation from a normalized logical data model to an
engineered-for-performance physical data model. The role of dimensional modeling and various physical design approaches are discussed in detail. Best practices in the use of surrogate keys is also discussed. The focus is
on understanding the benefit (or not) of various denormalization approaches commonly taken in data warehouse database designs.