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 […]

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 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 […]

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 […]

The notion of time pervades every corner of the data warehouse. Most of the fundamental measurements we store in our fact tables are time series, which we carefully annotate with time stamps and foreign keys connecting to calendar date dimensions. But the effects of time are not isolated just to these activity-based time stamps. All […]

The Kimball Group has been exposed to hundreds of successful data warehouses. Careful study of these successes has revealed a set of extract, transformation, and load (ETL) best practices. We first described these best practices in an Intelligent Enterprise column three years ago. Since then we have continued to refine the practices based on client […]

In this white paper, Ralph proposes a comprehensive architecture for capturing data quality events, as well as measuring and ultimately controlling data quality in the data warehouse. This scalable architecture can be added to existing data warehouse and data integration environments with minimal impact and relatively little upfront investment. Using this architecture, it is even […]

When developing fact tables, aggregated data is NOT the place to start. To avoid “mixed granularity” woes including bad and overlapping data, stick to rich, expressive, atomic-level data that’s closely connected to the original source and collection process. The power of a dimensional model comes from a careful adherence to “the grain.” A clear definition […]

With the current industry buzz focused on master data management (MDM), it’s time to revisit one of the most critical elements of the Kimball method. Back in 1999, Ralph Kimball wrote an Intelligent Enterprise column entitled The Matrix. The 1999 movie of the same name spawned two sequels, but we haven’t devoted a column to […]