This Design Tip describes how to create and manage mini-dimensions. Recall that a mini-dimension is a subset of attributes from a large dimension that tend to change rapidly, causing the dimension to grow excessively if changes were tracked using the Type 2 technique. By extracting unique combinations of these attribute values into a separate dimension, […]

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 […]

This article describes six key decisions that must be made while crafting the ETL architecture for a dimensional data warehouse. These decisions have significant impacts on the upfront and ongoing cost and complexity of the ETL solution and, ultimately, on the success of the overall BI/DW solution. Read on for Kimball Group’s advice on making […]

A student in a recent Data Warehouse Lifecycle in Depth class asked me for an overview of the Kimball Lifecycle approach to share with their manager. Confident that we’d published an executive summary, I was happy to oblige. Much to my surprise, our only published Lifecycle overview was a chapter in a Toolkit book, so this Design Tip […]

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: […]

A student attending one of Kimball Group’s recent onsite dimensional modeling classes asked me for a list of “Kimball’s Commandments” for dimensional modeling. We’ll refrain from using religious terminology, but let’s just say the following are not-to-be-broken rules together with less stringent rule-of-thumb recommendations. Rule #1: Load detailed atomic data into dimensional structures. Dimensional models […]

Successful data warehouse and business intelligence solutions provide value by helping the business identify opportunities or address challenges. Obviously, it’s risky business for the DW/BI team to attempt delivering on this promise without understanding the business and its requirements. This Design Tip covers basic guidelines for effectively determining the business’s wants and needs. First, start by properly preparing […]

Most ETL tools provide some functionality for handling slowly changing dimensions. Every so often, when the tool isn’t performing as needed, the ETL developer will use the database to identify new and changed rows, and apply the appropriate inserts and updates. I’ve shown examples of this code in the Data Warehouse Lifecycle in Depth class using standard INSERT […]

Fact tables are the foundation of the data warehouse. They contain the fundamental measurements of the enterprise, and they are the ultimate target of most data warehouse queries. There is no point in hoisting fact tables up the flagpole unless they have been chosen to reflect urgent business priorities, have been carefully quality assured and […]

The owner of the data warehouse must decide how to respond to the changes in the descriptions of dimensional entities like Employee, Customer, Product, Supplier, Location and others. In 30 years of studying this issue, I have found that only three different kinds of responses are needed. I call these slowly changing dimension (SCD) Types […]