The rise of Functional Programming
For many of us, Functional Programming (FP) is related to some boring computer science courses about lambda calculus and strange programming language like Lisp, Haskell or OCaml, used only by academic people and not by real programmers, who obviously use C or Java.
But FP is one of the hidden key concepts behind the "big data" revolution. It probably became mainstream when Google described, in their 2004 seminal paper, MapReduce as an “abstraction inspired by the map and reduce primitives present in Lisp and many other functional languages”. Now Twitter, LinkedIn, Akamai, TomTom, Erickson and others use modern functional languages such as Clojure (a Lisp dialect), Scala (a combination of Java and FP) or Erlang (a distant descendant of Prolog).
The main reason is that FP facilitates distributed programming. In FP, functions, not objects or procedures, are the fundamental building blocks of programs: whereas object programming put emphasis on object composition, FP put it on composition of functions. Functions can be combined, sent remotely, applied locally on distributed data sets, and that is the main paradigm shift of "big data". By eliminating side-effects and mutable variables and facilitating code distribution over several CPU, FP eases concurrent programming, a topic more and more important as the number of CPU core increases. FP makes also easier the development of internal Domain Specific Languages (DSL), these little languages embedded into applications simplifying the maintenance of business logic and improving programmer’s productivity.
Anyway it might be a good idea that software developers have explicit knowledge of principles of FP in order to write better code, such as avoiding side effects and instances variables, using immutable data structures, combining functions to be applied to objects. These skills might become more and more important in the future as FP patterns will spread.
So if during your next job interview the candidate appears to know what a monad is , you might consider hiring him with confidence for your next-generation framework.
 J. Dean and S. Ghemawat. MapReduce: Simplified Data Processing on Large Clusters. In OSDI’04, 6th Symposium on Operating Systems Design and Implementation, Sponsored by USENIX, in cooperation with ACM SIGOPS, pages 137–150, 2004.  http://importantshock.wordpress.com/2009/01/18/jquery-is-a-monad/.