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 […]
Kimball Design Tips
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 […]
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) […]
Many of you are already familiar with the data warehouse bus architecture and matrix given their central role in building architected data marts. The corresponding bus matrix identifies the key business processes of an organization, along with their associated dimensions. Business processes (typically corresponding to major source systems) are listed as matrix rows, while dimensions appear as matrix […]
Contrary to William Shakespeare and some data warehouse industry pundits, that’s NOT the question. In this article, we discuss an issue faced by maturing data mart/warehouse environments. While some organizations are newcomers to the data warehouse party, others have been at this for quite a while. As the market matures, the cause of data warehouse “pain” within the […]
The acronym, SCD, is a keyword in a dimensional modeler’s vernacular. As most of you know, SCD is short-hand for slowly changing dimensions. There are several well-documented techniques for dealing with slowly changing dimension attributes. Briefly, with SCD Type 1, the attribute value is overwritten with the new value, obliterating the historical attribute values. For example, when the […]
By Margy Ross and Bob Becker During the past year, we’ve repeatedly observed a pattern with maturing data warehouses. Despite significant effort and investment, some data warehouses have fallen off course. Project teams (or their user communities) are dissatisfied with the warehouse deliverables – the data’s too confusing, it’s not consistent, queries are too slow, etc. Teams have […]
One of the most prevalent fallacies in our industry is that data marts are defined by business department. We’ve seen countless data warehouse architecture diagrams with boxes labeled “Marketing Data Mart,” “Sales Data Mart,” and “Finance Data Mart.” After reviewing business requirements from these departments, you’d inevitably learn that all three organizations want the same core information, such as […]