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The competitive dynamics of the globalized market demand information on theinternal and external reality of corporations. Information is a precious assetand is responsible for establishing key advantages to enable companies tomaintain their leadership. However, reliable, rich information is no longer theonly goal. The time frame to extract information from data determines itsusefulness. This work proposes DOD-ETL, a tool that addresses, in an innovativemanner, the main bottleneck in Business Intelligence solutions, the ExtractTransform Load process (ETL), providing it in near real-time. DODETL achievesthis by combining an on-demand data stream pipeline with a distributed,parallel and technology-independent architecture with in-memory caching andefficient data partitioning. We compared DOD-ETL with other Stream Processingframeworks used to perform near real-time ETL and found DOD-ETL executesworkloads up to 10 times faster. We have deployed it in a large steelworks as areplacement for its previous ETL solution, enabling near real-time reportspreviously unavailable.

DOD-ETL: Distributed On-Demand ETL for Near Real-Time Business Intelligence