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Cloud computing with massive parallel executionengines is a common method to handle big data. But cloudcomputing and the characteristics of the data (semi structured, from different sources, probably cluttered) cause the developmentand execution of data processing jobs to be failure prone. Thisasks for processing engines to be fault tolerant. This paper introduces an approach to skip faulty or alreadyprocessed...
Parallel data processing frameworks are designed to give the user an easy, convenient way to process his code in parallel. Although giving a feedback of the status of a job is an important part of usability, progress estimation in parallel data processing is an issue that gets little attention. Knowing the estimated overall, remaining runtime will enable the user to monitor the job, thus get indications...
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