Kimball University: Keep to the Grain in Dimensional Modeling
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 of the grain of a fact table makes the logical and physical design possible; a muddled or imprecise definition of the grain poses a threat to all aspects of the design, from the ETL processes that fetch the data all the way to the reports that try to use the data.
What, exactly, is the grain? The grain of a fact table is the business definition of the measurement event that creates a fact record. The grain is exclusively determined by the physical realities of the source of the data (see related article, "Declaring the Grain").
All grain definitions should start at the lowest, most atomic grain and should describe the physical process that collects the data. Thus, in our dimensional modeling classes, when we start with the familiar example of retail sales, I ask the students "what is the grain?" After listening to a number of careful replies listing various retail dimensions such as product, customer, store and time, I stop and ask the students to visualize the physical process. The salesperson or checkout clerk scans the retail item and the register goes "BEEP." The grain of the fact table is BEEP!
Once the grain of the fact table is established with such clarity, the next steps of the design process can proceed smoothly. Continuing with our retail example, we can immediately include or exclude possible dimensions from the logical design of the retail fact table. Benefiting from the very atomic definition (BEEP), we can propose a large number of dimensions including date, time, customer, product, employee (perhaps both checkout clerk and supervisor), store, cash register, sales promotion, competitive factor, method of payment, regional demographics, the weather, and possibly others. Our humble little BEEP turns into a powerful measurement event with more than a dozen dimensions!
Of course, in a given practical application, the design team may not have all these dimensions available. But the power of keeping to the grain arises from the clarity that the grain definition supplies to the design process. Once the logical design has been proposed, the design team can systematically investigate whether the data sources are rich enough to "decorate" the BEEP event with all these dimensions in the physical implementation.
The Agile ArchiveWhen it comes to managing data, don’t look at backup and archiving systems as burdens and cost centers. A well-designed archive can enhance data protection and restores, ease search and e-discovery efforts, and save money by intelligently moving data from expensive primary storage systems.
2014 Analytics, BI, and Information Management SurveyIT’s tried for years to simplify data analytics and business intelligence efforts. Have visual analysis tools and Hadoop and NoSQL databases helped? Respondents to our 2014 InformationWeek Analytics, Business Intelligence, and Information Management Survey have a mixed outlook.
In this special, sponsored radio episode we’ll look at some terms around converged infrastructures and talk about how they’ve been applied in the past. Then we’ll turn to the present to see what’s changing.