One of the big topics in the BigData community is Map/Reduce. There are a lot of good blogs that explain what Map/Reduce does and how it works logically, so I won’t repeat it (look here, here and here for a few). Very few of them however explain the technical flow of things, which I at least need, to understand the performance implications. You can always through more hardware at a map reduce job to improve the overall time. I don’t like that as a general solution and many Map/Reduce programs can be optimized quite easily, if you know what too look for. And optimizing large map/reduce jobs can be instantly translated into ROI!

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