For most subject areas, it’s pretty easy to identify the major dimensions: Product, Customer Account, Student, Employee, and Organization are all easily understood as descriptive dimensions. A store’s sales, a telecommunication company’s phone calls, and a college’s course registrations are all clearly facts. However, for some subject areas, it can be challenging – especially for […]

This Design Tip continues my series on implementing common ETL design patterns. These techniques should prove valuable to all ETL system developers, and, we hope, provide some product feature guidance for ETL software companies as well. Recall that a shrunken dimension is a subset of a dimension’s attributes that apply to a higher level of […]

Some clients and students lament that while they want to deliver and share consistently-defined master conformed dimensions in their data warehouse/business intelligence (DW/BI) environments, it’s “just not feasible.” They explain that they would if they could, but with senior management focused on using agile development techniques to deliver DW/BI solutions, it’s “impossible” to take the […]

A data warehouse / business intelligence system is challenging to test. Standard testing methodology tests one little thing at a time, but a DW/BI system is all about integration and complexity, not to mention large data volumes. Here are my top five recommendations for building and executing a testing environment for your DW/BI project. 1. […]

Factless fact table are“fact tables that have no facts but captures the many-to-many relationship between dimension keys.” We’ve previously discussed factless fact tables to represent events or coverage information. An event-based factless fact table is student attendance information; the grain of the fact table is one row per student each day. A typical coverage factless fact […]