Atomic fact tables are the core foundation of any analytic environment. Business analysts thrive on atomic details because they can be easily rolled up “any which way” by grouping on one or more dimension attributes. The robust dimensionality of atomic data is extremely powerful as it supports a nearly endless combination of inquiries. However, business […]

Factless fact tables appear to be an oxymoron, similar to jumbo shrimp. How can you have a fact table that doesn’t have any facts? We’ve discussed the basics of factless fact tables several times in our books and articles. In this design tip, we use a factless fact table to complement our slowly changing dimension strategies. As you […]

People often engage us to conduct dimensional model design reviews. In this column, I’ll provide a laundry list of common design flaws to scout for when performing a review. I encourage you to use this list to critically review your own draft schemas in search of potential improvements. What’s the Grain? When a data warehouse team […]

The Kimball methodology of building a data warehouse is often called a bottom-up approach. This label, along with its associated connotations, is misleading, and misunderstandings about our approach are proliferating. It’s time to set the record straight: Although our iterative development and deployment techniques may superficially suggest a bottom-up methodology, a closer look reveals a […]

Intelligent Enterprise published an article entitled “The Bottom-Up Misnomer” in their latest September 17th issue. I wrote this article with Ralph several months ago. While it’s been working through the publishing pipeline, an industry newsletter has sparked more bottom-up versus top-down discussion. Everyone seems to feel qualified to explain the Kimball approach. Unfortunately, they sometimes spread misunderstandings and continue […]

As data warehouse designers, you know how important a business executive sponsor is to your initiative. After focusing on data warehousing for the past two decades, I’m convinced that strong business sponsorship is the leading make-or-break indicator of data warehouse success. Having the so-called right sponsor can overcome a multitude of shortcomings, such as difficulties […]

When developing a dimensional model, we often encounter miscellaneous indicators and flags that don’t logically belong to the core dimension tables. These unattached attributes are usually too valuable to ignore or exclude. Designers sometimes want to treat them as facts (supposed textual facts) or clutter the design with numerous small dimensional tables. A third, less obvious but preferable, solution […]

We are often asked about degenerate dimensions in our modeling workshops. Degenerate dimensions cause confusion since they don’t look or feel like normal dimensions. It’s helpful to remember that according to Webster, “degenerate” refers to something that’s 1) declined from the standard norm, or 2) is mathematically simpler. A degenerate dimension (DD) acts as a dimension key in […]

“Oh, we’ll handle that in the tool” is the refrain we sometimes hear from design teams. Instead, whenever possible, we suggest you invest the effort to architect as much flexibility, richness, and descriptive information directly into your dimensional schemas as possible rather than leaning on the capabilities of the tool metadata as a crutch. Today’s business intelligence (BI) […]

As the data warehouse market matures, the cause of data warehouse “pain” (otherwise known as vendor growth opportunity) within the IT organization is bound to evolve. Vendors promote centralization as a miracle elixir to treat data warehouse ailments. They claim it spins independent, disparate data marts into gold by reducing administrative costs and improving performance. […]