Too much to learn. Overwhelmed, incompetent, and behind

Bullshit... quit buying into that crappy old meme. At best, that might be sorta true IN Silicon Valley Proper, for a subset of companies. In the rest of the Real World, there's nothing particularly special about being 30 in tech.

If you're in SV and you buy that, then my advice would be move somewhere else. If you're not in SV, then quit worrying about being 30.

I'm feeling overwhelmed by the sheer number of technologies out there that I "should" learn: Python and R (and their libraries), Spark, Hadoop, TensorFlow, and pytorch. You also need to know how to productionalize things: Docker, AWS, distributed databases, the latest web stuff (sockets?), etc.

You don't have to know all of that stuff. A given firm, for example, probably uses Python OR R, but probably not both. Learn one or the other, you'll be fine. And not all roles require you to do everything from writing Dockerfiles to writing Map/Reduce code in Hadoop, to working with Tensorflow or PyTorch. Which bits of the stack you need really depend on the specifics of the job you wind up going for. But don't put that much of a burden on yourself to think that if you don't know every single thing on some "list" that you aren't employable.

Knowing inside out about all the tools is useless if you can't figure out how to apply them to the problem at hand. So I would suggest rather than running behind technologies, seek interesting problems to solve or interesting/better ways to solve already solved problems. Once you have catalog on what tech applies to which problems, you'll be much more valuable resource to a company and to yourself.

You can't fix a clogged drain using a hammer or that really fancy drill machine with twin battery pack. You can only solve it using a Plunger. If you know that, you can then go figure how to use one and after add it your resume not just saying 'you know how to use a Plunger' rather 'you solved a clogged drain using a Plunger', sounds better to potential recruiters.

If you can do meaningful data science in one language, and nothing else, you’re ahead of a whole bunch of curves. Get solidly good at one thing.

You’ll pick up a bit here and there about other techs in the course of the job. That will a) help you pick the highest leverage next thing to learn, and b) anchor you when you start learning them.

Stick to a theme. You don’t need to know web sockets to do data science.

For followup learning, pick complementary technologies (instead of Python and R, which compete; or R and and Docker, which are virtually unrelated; try either Python plus tensorflow or R plus tensorflow — because either of those pairings can be used together to increase your capabilities.)

I don't have many answers for you on tech. But I can tell you that anywhere outside of tech companies, coding is a superpower. From this perspective, it helps to move away from thinking about tools, to thinking about what you want to achieve with them. Forget about others and their paychecks? What gives you a kick? Start noticing which types of problems you enjoy solving, a narrative will grow out of that.

(As someone who's been coding in python for years and still is nowhere near as good as a proper good pythoner, I see no point of investing any time in R. It's one or the other. Same for Tensorflow and PyTorch. I try and work with PyTorch because smarter people than me have chosen to, and you just have to pick a road to start walking. But I don't see myself as any form of authority on tech issues, so...)

When you'll be 40, you'll think 40 is old and 30 was young. At 50, you'll think 40 was young and 30 was practically a baby. Why wait till then? Might as well start thinking it this way now - you are young.

And lastly, keep reminding yourself: Comparison is the thief of joy. People earning twice as much as you are not happier, not at these pay scales.

I'm not based in the US and salaries here, even for experienced developers, are nowhere near the numbers you see thrown around.

As for the salary comparisons. Look at people at your location and with comparable skills for your reference. Large discrepancies are likely to be due to location and employer factors rather a reflection upon your true value.