Common Myths

There are several persistent misconceptions about dimensional modeling, often stemming from confusion between different data warehouse architectures. Reviewing these myths at the end of this chapter provides a good summary of the principles of dimensional modeling.

Myth 1: Dimensional Models are Only for Summary Data

Reality: Dimensional models should always be built on the lowest possible grain of data (atomic data). Summary tables (aggregates) are added later purely for performance tuning, but the foundation must be atomic to answer unpredictable, granular queries.

Myth 2: Dimensional Models are Departmental, Not Enterprise

Reality: While a dimensional model might be deployed incrementally (one business process or department at a time), it is absolutely an enterprise-wide architecture. Conformed dimensions (shared dimensions like Date or Customer) stitch these incremental models together into a cohesive enterprise data warehouse.

Myth 3: Dimensional Models are Not Scalable

Reality: Dimensional models scale exceptionally well. Fact tables can handle billions of rows. The star schema design is highly optimized for modern relational database engines, which easily partition and distribute these large tables.

Myth 4: Dimensional Models are Only for Predictable Usage

Reality: By providing data at the lowest atomic grain and standardizing the schema structure, dimensional models allow business users to ask highly unpredictable ad-hoc queries. They are not restricted to pre-canned reports.

Myth 5: Dimensional Models Can't Be Integrated

Reality: The entire architecture relies on integration. Because dimensions are shared (conformed) across different fact tables, data from completely different business processes can be seamlessly integrated and queried together.