It's not clear that this comment is addressing any real issues.
Firstly, std::unordered_map is officially part of C++ now, and it's been available in all major compilers for a long time. Secondly, there's no substantial evidence provided to support the claim that the module system in a dynamic language is "more flexible" - anytime you want to include someone else's code in your source tree in any language you can just drop the source in as though it was your own. Whether there's a "package manager" to hide the references for you is beside the point; it's not as if the concept of shared libraries is lost upon C and C++.
And "ahem" does not establish a valid argument as to why dynamic type checking is "easier" (whatever that even means). In fact, I believe a strong argument could be made that dynamic type checking is the worst aspect of dynamic languages, and the interest that Haskell has been brewing up lately would tend to support this. Static type checking brings bugs to the forefront when the developer is in the room instead of the user. One could argue that runtime reflection provides more flexibility and thus makes dynamic languages "easier", but it's a stretch to extend that to dynamic typing.
I'm unsure of how DLL Hell would even apply to a SaaS application, unless we're referring to completely different things; you have complete control of your application's environment in a web app. If there's a package already installed that you don't want or something is missing, you remove it or install it. With the prevalence of virtualization and virtualization-as-a-service, there's no reason to be trying to run two different applications that require different dependencies on the same virtual box.
I subscribe to the school of thought that you learn by doing, so my best advice is to put yourself out there and write some code. I've read lots of blogs, and a small handful of programming books (K&R, parts of SICP, parts of CLRS, Programming Pearls), but at each step I took it upon myself to actually do what the author was doing. Reading code or watching lectures isn't going to make you a better programmer any more than watching tennis will make you a better tennis player. You presumably already know the rules, you just need to practice to get to the next level.
Try writing anything that sounds fun. Write your own JSON parser, or a trie, or a B+ tree, or an implementation of the travelling salesman or knapsack problems. Wikipedia will get you started on all of these, and from there you can write the code. Too often people promote the idea that "Well, JSON libraries exist, why write your own?", but that misses the point. For one, it's fun. Professional tennis players exist too, and I am definitely never going to be as good as they are, but that doesn't mean it's not worth playing tennis if I enjoy doing so. More importantly, though, if you've written your own JSON parser, then the next time a new protocol or file format comes out and your language of choice isn't officially supported, you'll be able to write your own and be ahead of the game. All of these skills are transferable.
An added bonus is that when you go to apply for programming jobs, your portfolio of fun side projects will speak volumes about your ability to code. Don't worry that you don't have the best, most efficient, most popular JSON parser. The important part is that you spent your personal time bettering yourself, and that you are interested in being great at what you do.
After I saw your first comment, I started writing out all billion numbers. It appears to be getting exponentially slower, which I suppose is to be expected as the file gets too big to fit in available contiguous blocks (there was only 100 GB free before it started).
It's been running for about an hour and is only half done, although writing 7 GB files would definitely be much faster; I don't know if sort --merge creates one big file at the end or not, but there are ways around that since it would be the bottleneck. It does sound like it could be done with sort in a semi-reasonable amount of time, although there'd still be more work to do once it's sorted.
In terms of writing the code itself, the parser is pretty similar to a toy computer algebra system I had written in the past (it's much simpler), so I had some pre-disposition to it in that sense, and the traversal is fairly straight forward given the grammar. I chose a problem that I thought I could solve in an afternoon because I knew I had real work to do the following Monday and still had to write the blog post, and Parkinson's Law may have helped me push through it a bit too. :)
Edit to reply to edit: This puzzle is actually retired from ITA's fleet, and I'm not interested in applying to ITA/Google anyway. I'm happily employed, and actually this is sort of the reverse: I'm looking to hire remote software devs at BuySellAds.com and was hoping this would pique the interest of the qualified segment of devs looking for a job.
I didn't have 70 GB of disk space free (two small SSDs in my laptop), but I just started running it on my old machine and will let you know the results. The grammar, parser, and traversal functions took me somewhere between two and three hours in total to think up and write; my gut feeling is that writing and sorting 70 GB of data on a spinning disk would be slower, though I may be wrong.
Another issue with using sort that you still need the sum of the numbers. You could store this as "wordnum;realnum", but now you've got over 100 GB of data to sort, and you still need to parse this out when you do your single pass after to get to the correct byte count. You could spend some time optimizing the sorting format (again using tokens), but now you're spending time doing work that's getting you even closer to an actual solution in code.
The coded solution is also scalable, although you could argue the necessity of scaling in this context. If a company were to introduce this puzzle looking for some byte in the first trillion numbers sorted (instead of billion), you'd be looking to sort several terabytes of data on disk; the constant memory solution, once tweaked to include the billions case in the same way the millions case is handled, would take roughly 15 minutes to run.