It seems like a small thing, but names are important. Good names for tables and columns are particularly important for ad hoc users of the DW/BI system who need to find the objects they’re looking for. Object names should be oriented to the business users, not the technical staff. As much as possible, strive to […]
Kimball Design Tips
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. […]
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 […]
The increasingly popular data visualization tools deliver an environment that business analysts love. They provide the ability to define calculations, and more importantly, to explore and experiment with the data. The products have finally innovated away from the old standbys of tables, bar charts, and pie charts, making it easier for users to draw visual […]
One of the key components of the business intelligence (BI) architecture is a semantic layer. The semantic layer provides a translation of the underlying database structures into business user oriented terms and constructs. It is usually part and parcel of the query and reporting tool. OLAP or cube databases also include a BI semantic layer. […]
Drilling across separate business processes is one of the most powerful applications in a data warehouse. We often describe drilling across as magic: separately open connections to the dimensional models for each business process, fetch answer sets from each process labeled identically with row headers drawn from specially conformed dimensions, then deliver the result by […]
Many organizations are embracing agile development techniques for their DW/BI implementations. While we strongly concur with agile’s focus on business collaboration to deliver value via incremental initiatives, we’ve also witnessed agile’s “dark side.” Some teams get myopically focused on a narrowly-defined set of business requirements. They extract a limited amount of source data to develop […]
Ralph introduced the concept of slowly changing dimension (SCD) attributes in 1996. Dimensional modelers, in conjunction with the business’s data governance representatives, must specify the data warehouse’s response to operational attribute value changes. Most Kimball readers are familiar with the core SCD approaches: type 1 (overwrite), type 2 (add a row), and type 3 (add […]
One of the tasks of the ETL system’s customer dimension manager is to “assign a unique durable key to each customer.” By durable key, we mean a single key value that uniquely and reliably identifies a given customer over time. In most cases, this unique durable key is the natural business key from the operational […]