There are three fundamental types of fact tables in the data warehouse presentation area: transaction fact tables, periodic snapshot fact tables, and accumulating snapshot fact tables. Most DW/BI design teams are very familiar with transaction fact tables. They are the most common fact table type and are often the primary workhorse schema for many organizations. […]

In Design Tip #140, I discussed the challenges of designing dimensional schemas for processes of indeterminate length such as a sales pipeline or insurance claims processing. We concluded they are best represented as accumulating snapshot fact tables characterized by one row per pipeline occurrence where each row is updated multiple times over its lifetime. However, […]

For most subject areas, it’s pretty easy to identify the major dimensions: Product, Customer Account, Student, Employee, and Organization are all easily understood as descriptive dimensions. A store’s sales, a telecommunication company’s phone calls, and a college’s course registrations are all clearly facts. However, for some subject areas, it can be challenging – especially for […]

It’s been awhile since we talked about factless fact tables in Design Tip #50. You may recall that a factless fact table is “a fact table that has no facts but captures the many-to-many relationship between dimension keys.” We’ve previously discussed factless fact tables to represent events or coverage information. An event-based factless fact table […]

We always want our business users to have confidence in the data we deliver through our data warehouses. Thus it goes against our instincts to talk about problems encountered in the ETL back room, or known inaccuracies in the source systems. But this reluctance to expose our uncertainties can ultimately hurt our credibility and lessen the business users’ […]

My previous column described the three types of fact tables that are all you will ever need in your data warehouse (see December 2008 DM Review). The secret to this simple observation is adhering fanatically to the grain. A fact table records measurement events, and as long as we only record one kind of measurement event […]

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. Perhaps you are wondering why it took me so long to get to fact tables in DM Review? Well, there is no point in hoisting fact tables […]

In the dimensional modeling world, we try very hard to separate data into two contrasting camps: numerical measurements that we put into fact tables, and textual descriptors that we put into dimension tables as “attributes”. If only life were that easy… Remember that numerical facts usually have an implicit time series of observations, and usually participate in numerical […]

Many transaction processing systems consist of a transaction header “parent” with multiple line item “children.” Regardless of your industry, you can probably identify source systems in your organization with this basic structure. When it’s time to model this data for DW/BI, many designers merely reproduce these familiar operational header and line constructs in the dimensional world. In this Design […]

In the dimensional modeling world we talk about three kinds of fact table grains: transaction, periodic snapshot, and accumulating snapshot. If you are careful to make sure every fact table you design is based on one grain, and doesn’t mix different grains, amazingly these three choices are enough to design every fact table in your data warehouse. The […]