[spinron]

Some of you might have missed the perspective of the article's author (perhaps it isn't that clear); you might want to re-examine it from an implementer point of view. In other words, if you have a real-life big data problem (that would benefit from parallel processing via Hadoop) and you actually have to build the thing so that it would work and scale, the decomposition presented by the architecture would make the implementation a lot simpler. Architecting such systems isn't trivial, and this is a solid blue-print to start with. And it's really an architecture, not a model: It doesn't tell you how to formulate algorithms, it rather suggests how to build a complete system around them.

I have read the recent draft of the "Big Data" book by the author, which describes the architecture that the article discusses in better detail. Honestly, if you are a beginning practitioner in this field, you can't really go wrong by reading it.