What is the novelty in MapReduce?

A few years ago, MapReduce was hailed as revolution of distributed programming. There have also been critics but by and large there was an enthusiastic hype. It even got patented! [1] The name is reminiscent of map and reduce in functional programming, but when I read (Wikipedia) Map step: The master node takes the input, divides it into smaller sub-problems, and distributes them to worker nodes. A worker node may do this again in turn, leading to a multi-level tree structure. The worker node processes the smaller problem, and passes the answer back to its master node. Reduce step: The master node then collects the answers to all the sub-problems and combines them in some way to form the output – the answer to the problem it was originally trying to solve. or [2] Internals of MAP: [...] MAP splits up the input value into words. [...] MAP is meant to associate each given key/value pair of the input with potentially many intermediate key/value pairs. Internals of REDUCE: [...] [REDUCE] performs imperative aggregation (say, reduction): take many values, and reduce them to a single value. I can not help but think: this is divide & conquer (in the sense of Mergesort), plain and simple! So, is there (conceptual) novelty in MapReduce somewhere, or is it just a new implementation of old ideas useful in certain scenarios? US Patent 7,650,331: "System and method for efficient large-scale data processing " (2010) Google’s MapReduce programming model — Revisited by R. Lämmel (2007)