The Hadoop Summit 2015 talk below introduces G-OLA, a parallel approximate query engine built on top of BlinkDB and SparkSQL, that provides a radically different “online execution” paradigm to incrementally process massive amounts of data on clusters of hundreds or thousands of machine while returning approximate answers. G-OLA presents the user with a meaningful approximate result (with error bars) that is continuously refined at a speed comfortable to the user and enables them to control the query execution on the fly. The slides for this presentation are available HERE.
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Can spark be used for generate sequential patterns from dynamic streams of big data( especially considering dna sequences as data set)?