Dimensional designs often need to accommodate multivalued dimensions. Patients can have multiple diagnoses. Students can have multiple majors. Consumers can have multiple hobbies or interests. Commercial customers can have multiple industry classifications. Employees can have multiple skills or certifications. Products can have multiple optional features. Bank accounts can have multiple customers. The multivalued dimension challenge […]

One of the most effective tools for managing data quality and data governance, as well as giving business users confidence in the data warehouse results, is the audit dimension. We often attach an audit dimension to every fact table so that business users can choose to illuminate the provenance and confidence in their queries and […]

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 […]

A junk dimension combines several low-cardinality flags and attributes into a single dimension table rather than modeling them as separate dimensions. There are good reasons to create this combined dimension, including reducing the size of the fact table and making the dimensional model easier to work with. Margy described junk dimensions in detail in Kimball Design Tip #48: […]

Students often blur the concepts of snowflakes, outriggers, and bridges. In this Design Tip, I’ll try to reduce the confusion surrounding these embellishments to the standard dimensional model. When a dimension table is snowflaked, the redundant many-to-one attributes are removed into separate dimension tables. For example, instead of collapsing hierarchical rollups such as brand and category into columns […]

The global data warehouse introduces a whole new world of design issues  As soon as the geographic spread of our data warehouse crosses a time zone or a national boundary, a whole host of design issues arise. For the sake of a label, let’s call such a warehouse a global data warehouse, and let’s collect all […]

Insurance is an important and growing sector for the data warehousing market. Several factors have come together in the last year or two to make data warehouses for large insurance companies both possible and extremely necessary. Insurance companies generate several complicated transactions that must be analyzed in many different ways. Until recently, it wasn’t practical […]