Not Java/Spring?

Google at least is known to use C/C++ for its backend. Python is (or was?) used in YouTube.

I think java comes out as one of the most efficient and battle tested backend languages.

For data crunching, especially statistical jobs, Python is probably also a good fit although it's not as efficient but have a number of good statistical, mathematical and ai toolkits that seems to be more active.

For running more specialized jobs, R have even more tools, but is a bit more shaky as a general language although it is a nice functional language, as many tools are more scientists scaffolding rather than nice abstractions.

Then you have fortran and matlab. I've heard of using erlang as a fast real-time analytics tool too, but then you probably need to roll your own everything.

But what do you suggest? Node.js claim to fame is mainly that most web developers know it, and js have formed their mental image of what programing is and how a computer works.

Just curious, do you have direct experience with big companies using R, python, etc in production? My sense from working with people from those companies (and a few internships at those companies) is that you could use something like R, matlab, or scikit-learn on your own workstation with a tiny data sample to explore the data, but then do crunching by translating that program into some Java or C++ code (sometimes using specialist libraries) and running that in parallel, for production.

Do people actually just skip that step and just directly deploy their R? That seems really scary.

Having worked with several large (fortune 500) data science teams, they generally do their development in Python and then throw their models over the fence for us to productionize with Java.

The major difference I've seen between most of these companies is whether they've embraced Java 8 yet.

This is my experience as well. The data science people, who need to do proofs of concept, use Python. The people who have to write large, complicated codebases that scale well and accept years of modification use statically-typed languages like Java and C#.

To be honest, until recently I'd say that everything would be rewritten in java - mostly since there were already deployment pipes for it.

With the latest container-based toolchains I think it's not a bad idea to deploy some R-based services for certain type of tasks.

With the work Microsoft have been doing with the former Revolution Analytics, you absolutely can go into Production on R if you want to.

A few companies ago I worked at a company like that - researchers in R, MATLAB, Excel, whatever, then once it worked chuck it over the fence to the developers to rewrite in C++ or Java. Then the CTO, who was a visionary guy, made the decision that we would be all Python end-to-end with occasional C permitted for performance-critical sections. There were some teething troubles at first but when it worked, it was wildly successful and no-one could believe we'd ever worked any other way.

What is wrong with spring other then "not hipster compliant"?

Google has quite a lot of java.

Google had and has a lot of Java, see GWT. It might not be hip anymore, but its still widely used in a lot of backend environments.

Google uses lots of C++ for very performance critical parts (search, browser) and then lots of Java everywhere else.

I dunno about Spring in specific, but a quick search for 'java' on amazon.jobs brings back ~2700 open positions. The google search doesn't seem to give a total. Apple brings up ~500.

Java is huge in a lot of places.