Accumulating snapshots are one of the three fundamental types of fact tables. We often state that accumulating snapshot fact tables are appropriate for predictable workflows with well-established milestones. They typically have five to ten key milestone dates representing the workflow/pipeline start, completion, and the key event dates in between. Our students and clients sometimes ask […]

Design Tip #43 Dealing with Nulls in the Dimensional Model describes two cases where null values should be avoided in a dimensional model; in these situations, we recommend using default values rather than nulls. This Design Tip provides guidance for selecting meaningful, verbose defaults. Handling Null Foreign Keys in Fact Tables The first scenario where […]

Industry-standard data models are an appealing concept at first blush, but they aren’t the time savers they are cracked up to be. What’s more, these prebuilt models may inhibit data warehouse project success. Vendors and proponents argue that standard, prebuilt models allow for more rapid, less risky implementations by reducing the scope of the data […]

If you’re a long time reader of the Kimball articles, Design Tips and books, you know we feel strongly about the importance of understanding the business’s data and analytic requirements. Suffice it to say that we expect more than merely inventorying the existing reports and data files; we encourage you to immerse yourself in the business community […]

The Kimball Group has frequently written about the importance of focusing on business requirements as the foundation for a successful DW/BI implementation. Design Tip #110 provides a crisp set of dos and don’ts for gathering requirements. However, some organizations find it difficult to land on the right level of detail when documenting the requirements, and […]

Whether you are developing a new dimensional data warehouse or replacing an existing environment, the ETL (extract, transform, load) implementation effort is inevitably on the critical path. Difficult data sources, unclear requirements, data quality problems, changing scope, and other unforeseen problems often conspire to put the squeeze on the ETL development team. It simply may […]

Over the years, we’ve described common dimensional modeling mistakes, such as our October ’03 “Fistful of Flaws” article in Intelligent Enterprise magazine. And we’ve recommended dimensional modeling best practices countless times; our May ’09 “Kimball’s Ten Rules of Dimensional Modeling” article has been widely read. While we’ve identified frequently-observed errors and suggested patterns, we haven’t […]