![]() Secondly, very often we don't need to deduce patterns over long periods of time. But petabytes take long to analyze, even with Hadoop (as good as MapReduce may be) or Spark (a remedy to the limitations of MapReduce). Of course this data is stored, processed and analyzed to provide predictive, actionable results. I'm also assuming that you're at least somewhat familiar with Docker and containerization.Ĭontinuous streams of data are ubiquitous and becoming even more so with the increasing number of IoT devices being used. A few words on message processing reliability.Setting up and playing with a production-worthy Storm cluster on Docker.What a Storm topology roughly looks like in code (Java).The necessity of Storm, the 'why' of it, what it is and what it isn't. ![]() OK, now that that's out of the way, let's see what we'll be covering: Of course, any additions, feedback or constructive criticism will be greatly appreciated. ![]() ![]() Storm's pretty huge, and just one long-read probably can't do it justice anyways. This article is not the ultimate guide to Storm nor is it meant to be. ![]()
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