I write my MapReduces in C++ the first time. Why? Because writing them typically takes only an hour or two. Running them can easily take a couple days. If I take a 10x productivity improvement for a 10x execution slowdown, my development time goes from 3 hours to 20 minutes, but my execution time goes from a couple days to a month. Not really a great tradeoff.
I'm not a C++ programmer and do not know the complexity involved with a map reduce, but surely there is still a greater chance that with C++ that you will shoot yourself in your foot (or chainsaw, take your pick).
I'd say the chance that you shoot yourself in the foot is roughly equal with both languages, but with Python, it's far more likely that 'tis but a flesh wound. With C++, you're likely to sever an artery and need extensive vascular repair.
The point is that with small programs that operate on big data, the cost of shooting your foot is less. There's less code to wade through, so you can find and fix your bugs quickly, and it still costs you less time than you'll lose in execution speed.
Other types of programs have different complexity/execution tradeoffs, and other languages may be more appropriate for them. I actually do the majority of my programming in Python - but that's for other things, where I have to iterate rapidly yet am typically the only one hitting my server. That's very different from a program that you'll write once, run once, and never have to maintain again. (Or one that you write once, run many times, and never maintain again...which describes a bunch of other pieces of code.)
Constant factors can matter.