How do you deal with changing dimensions? Hybrid approaches fill gaps left by the three fundamental techniques. Unlike most OLTP systems, a major objective of a data warehouse is to track history. So, accounting for change is one of the analyst’s most important responsibilities. A sales force region reassignment is a good example of a […]

Do you know the difference between dimensional modeling truth and fiction? According to Merriam-Webster, fables are fictitious statements. Unfortunately, fables about dimensional modeling circulate throughout our industry. These false claims and assertions are a distraction, especially if you’re trying to align a team. In this column, we’ll describe the root misunderstandings that perpetuate these myths so […]

The Kimball bus architecture and the Corporate Information Factory: What are the fundamental differences? Based on recent inquiries, many of you are in the midst of architecting (or rearchitecting) your data warehouse. There’s no dispute that planning your data warehouse from an enterprise perspective is a good idea, but do you need an enterprise data […]

Virtually every fact table has one or more time related dimension foreign keys. Measurements are defined at specific points of time and most measurements are repeated over time. The most common and useful time dimension is the calendar date dimension with the granularity of a single day. This dimension has surprisingly many attributes. Only a few of […]

I worked at a company called Metaphor back in the early 1980s. As part of a startup software company introducing then-cutting-edge concepts (such as folders, file drawers, and workflows on a graphical electronic desktop), I appreciated the benefits of using metaphors to represent seemingly complex concepts. Effective verbal and visual metaphors are able to convert complexity into […]

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 […]

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 […]

The three fundamental themes that make up the soul of every data warehouse are drilling down, drilling across, and handling time. In Part One of “The Soul of the Data Warehouse,” I showed that drilling down was nothing more than adding a row header, any row header, to an existing query. Although we often grouse about […]

Although data warehouses come in many shapes and sizes and deal with many different subject areas, every data warehouse must embody a few fundamental themes. The three most important are drilling down, drilling across, and handling time. Modern data warehouses so deeply embed these three themes that I think an “if-and-only-if” relationship has developed between […]

In debugging literally thousands of dimensional designs from my students over the years, I have found that the most frequent design error by far is not declaring the grain of the fact table at the beginning of the design process. If the grain isn’t clearly defined, the whole design rests on quicksand. Discussions about candidate […]