161–170 of 430 posts
I'd suggest go work somewhere else.
Web tooling is better than ever. I can very quickly spin up a full-fledged production grade app with very little investment. I don't worry about blockchain or NoSQL or any of that. I just use tools that make me a productive engineer and that's ultimately what companies are interested in. If you're worried about recruiters asking you if you've looked at modern languages, then you've got some bigger fish to fry. If you don't know the language, the answer you should feel like giving is "I can learn anything, and I'd be happy to prep for the job."
I'm currently working on a statistics website for a game named Smite. The ingestion engine is powered by Go/Redis/PSQL/Docker, and the frontend is Next.js deployed on Render.
This is hardly complex. The Go binary reaches out to the Hirez API service, requests some data, caches it on Redis (in case we need to run the ingestion multiple times during development and to avoid service quotas), and then stores the data in a normalized data structure in Postgres. With Postgres I can now run SQL queries on top to gather stats about the playerbase and the games. All of this is done on my local machine. My MacBook has about 1 TB of hard disk space, which used to be unheard of a couple of years ago, so I have no worries about my database growing to a size I can't manage (old matches are also pruned and removed).
The next part is the frontend part, which is what I'm working on now. But this is also super simple. I'm using Next.js to statically render a website using SSG. I basically reach out to the Postgres database locally, grab the data points I need, render the UI into static HTML files, and then I just take that build, push it to Git and it triggers a job to deploy it on Render. All of this tooling is ridiculously and refreshingly simple.
I think you're really overthinking it.
Earlier quoted context omitted.
Promotion based architecture is self fulfilling prophecy at least in BI/ data world. I see everybody around me moving to cloud, without really good explanation why. Only reasonable thing I can see as an pattern is that cloud experience on top of data things gets paid 30% more. It made me consider cloud a lot. I was considering switching to cloud, just so I can put in my CV "experience with migration to cloud". For ne…
>For next person commenting that it makes sense: it doesn't with 200gb database and super predictable workload, growth and usage. Depends on the company. I've been working for marketing agencies for the last 15 years and they're generally staffed by, at most, 1 IT person who is in charge of a third-party vendor relationship that offers managed IT services. Those IT resources (internal or vendor) don't specialize in d…
For anything above what we have I think it weirdly depends on country/ salaries.
In US, 100k per year is no brainer for cloud as even 1 FTE would cost much more.
In non-western Europe, 100k is deal killer as you can hire 2 senior DBAs and you still have enough money for quite a reasonable server.
I don't think we're screwed.
I agree at times complex solutions are prioritized for the wrong reasons - e.g to create more work, buzzwords, or to look nice for hiring and investors. But ultimately these are tools with tradeoffs.
I happen to like K8s, monorepo, and Go because they solve problems that I have personally run into. I think crypto goes too far and doesn't really solve anything.
In terms of complexity, I don't see these tools as going from algebra to calculus, but more like re-learning variations of algebra over and over - sure its tedious, but its not rocket science.
However if you don't like dumb industry trends that don't create business value you can always go work for a series A startup. They DGAF about the frills or buzzwords, they just want fast results.
The dangerous spot is engineers with 5-10 years of xp who have become good enough at writing huge piles of unecessary code and have them work.
I'll take the devil's advocate: * All these new tools, they give us options, no? Use the right tool for the job, the ability to switch if something becomes old/unmaintained. * Is this actual complexity or perceived complexity given your experience? The node ecosystem looked very complex for me (someone coming from Python) until I actually got into it. Now it seems pretty run-of-the-mill. * Is k8s really all that hard…
No, the deployment of a container to Kubernetes isn't hard. And it better not be, that's supposed to be the advantage.
What's hard is literally everything else about it. And that may be a fine tradeoff if you are at the scale to need it and have a team to manage it. But there are many organizations where that tradeoff does not make sense.
Phone calls are stupid complex nowadays compared to the old point-to-point wiring, but we can still very easily "pick up the phone and dial." It's an abstraction/mental model that's held since PBXs became automated.
When I studied machine learning 20 years ago, it was barely used, and everything was "from basics." The applied stuff was very simple, like an auto-encoder. Today, the way you think about, and teach, ML is not "a matrix here and a vector there," but in combinations of ANN layers.