Data Warehouse Management with SAP BW


The entire course will emulate a real-world warehouse implementation project from its early planning stages to final use. The system used will be SAP's Business Warehouse (SAP BW). SAP is the world market leader of Enterprise Resource Planning systems (SAP ECC), but also offers a data warehouse product which can be used indepentently of ECC.

Learning Outcomes

"Datawarehouse Development with SAP BW: An Introduction" and "Datawarehouse Development with SAP BW: Balanced Scorecard" form a unit that prepares students for the complex tasks of building and using systems for business analysis and simulation. In "Datawarehouse Development with SAP BW: An Introduction" students learn how to design and implement a data warehouse as well as decision support and reporting functions on top of the warehouse. This course lays the foundations for the strategic enterprise management and business simulation in "Datawarehouse Management with SAP BW: Balanced Scorecards." The course starts with the methodological foundations that are necessary to transform a user requirement for a decision support system into a data warehouse design specification:

  • Dimensional Fact Modeling: extraction of a basic warehouse model from information on operational IS,

  • Aggregation Path Array: planning the aggregation hierarchies to support specified reporting requirements,

  • Logical Model specification of the warehouse.


Each method is immediately applied in a group assignment for a given technical specification and business problem, resp. Students then learn to implement the specification incorporated in the above models in a data warehouse product, SAP BW. Each student works in a separate virtual data warehouse implementing the system from scratch. The implementation steps are:

  • Defining the multi-dimensional data structures, the time series, and the aggregation hierarchies for high-level aggregates, which are needed for the analytical applications of the data warehouse,

  • Defining sources for data imports, data validation and reconciliation schemas,

  • Physically loading the warehouse using pre-arranged data thereby filling the above data definitions,

  • Defining procedures for periodical data update and the refresh of aggregate data in the warehouse.


For DFM modelling cf. Golfarelli, M., Rizzi, S., Maio, D., The Dimensional Fact Model: A Conceptual Model for Data Warehouses