Dimensional Data Modeling Primer: From Requirements to Business Analysis
Dimensional data is a core component of modern business intelligence and data warehouse implementations. Dimensionally organized data offers a more effective and adaptable solution to business analytics needs than can be achieved with relational data structures. Virtually anyone involved in business intelligence and data warehousing projects needs to have fundamental knowledge of the pathway from business questions to business analytics. This course traces that pathway.
The course begins with a comparison of relational and dimensional data organization and provides an example of business questions not readily answered using more traditional data structures of relational modeling. It then illustrates the steps to design analytic solutions, starting from business questions and concluding by demonstrating an OLAP solution. These steps encompass techniques to capture business questions, represent them as a business solution, translate them into a technology solution, and deliver them to those who need information.
You Will Learn
- Concepts of dimensional data modeling
- The relationship between business metrics and dimensional data
- Similarities and differences between relational and dimensional data models
- Requirements-gathering techniques for business metrics and dimensional data
- How to build a logical dimensional model
- How to translate a logical dimensional model to a star schema design
- How dimensional data is used to deliver business analytics and OLAP capabilities
- Data architects
- Data mart developers
- Business analysts
- Business intelligence and data warehouse program and project managers
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