On-Demand ELT Architecture for Right-Time BI: Extending the Vision

Florian Waas, Robert Wrembel, Tobias Freudenreich, Maik Thiele, Christian Koncilia and Pedro Furtado
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Florian Waas: EMC Corporation, Hopkinton, MA, USA
Robert Wrembel: Faculty of Computing, Poznan University of Technology, Poznan, Poland
Tobias Freudenreich: Department of Computer Science, Technische Universität Darmstadt, Darmstadt, Germany
Maik Thiele: Database Technology Group, Technische Universität Dresden, Dresden, Germany
Christian Koncilia: Institute for Information-Systems, University of Klagenfurt, Klagenfurt, Austria
Pedro Furtado: Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal

International Journal of Data Warehousing and Mining (IJDWM), 2013, vol. 9, issue 2, 21-38

Abstract: In a typical BI infrastructure, data, extracted from operational data sources, is transformed, cleansed, and loaded into a data warehouse by a periodic ETL process, typically executed on a nightly basis, i.e., a full day’s worth of data is processed and loaded during off-hours. However, it is desirable to have fresher data for business insights at near real-time. To this end, the authors propose to leverage a data warehouse’s capability to directly import raw, unprocessed records and defer the transformation and data cleaning until needed by pending reports. At that time, the database’s own processing mechanisms can be deployed to process the data on-demand. Event-processing capabilities are seamlessly woven into our proposed architecture. Besides outlining an overall architecture, the authors also developed a roadmap for implementing a complete prototype using conventional database technology in the form of hierarchical materialized views.

Date: 2013
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