I'm not a "power-user", but I'm in the process of building a pretty big product powered by hadoop (more specifically, mrjob and amazon's EMR). I have a hard ti

I'm not a "power-user", but I'm in the process of building a pretty big product powered by hadoop (more specifically, mrjob and amazon's EMR). I have a hard time reading this as anything but satire. The author definitely knows a lot about hadoop, yet displays a misunderstanding of such a basic piece of it (conflating hadoop with map-reduce). Just a few key things I can refute: Hadoop as wasteful: production systems are built to withstand the expected peaks of traffic (i.e., some website that sees a huge influx when the east coast wakes up on monday morning will design with that load+buffer in mind), which means that ~90% (probably even higher!) of the time resources are underutilized. even personally my own laptop isn't chugging at 100% on all cores all day every day, I don't see how anybody expects any collection of computers to do so. On the flip side, this is being offset by platforms like AWS. When I run map-reduce jobs on hadoop, I spin up an EMR jobflow, run a few tasks and then shut it down. Presumably, somebody else gets to use those machines after I'm done. Lack of third party support: Honestly, with mrjob and EMR, I've never needed to deal with any hadoop management, setup, maintenance or scaling. Also, all my code is python, I've not had to write a line of Java. And these aren't the only tools built on top of hadoop. Hadoop as deprecated: there are certainly more big-data crunching platforms coming out nowadays, but hadoop is mature, stable, and still going strong. Also, the basic map-reduce framework it's based on is not going anywhere soon. I mean, I cut my teeth on map-reduce with mongodb, and it seems most other nosql solutions there are going that route for complicated things their standard query engine can't support.