I’ve been in the same job for a long time, and using, broadly, the same tech stack. Some of this tech still sounds modern and relevant (Kubernetes, Docker, Typescript etc.), other bits not so much.
I’d like to understand better how the industry at large is developing, so here I’m going to create a list of technologies I’d like to look into, at least to understand the basics. I hope to write a little piece as I do it about my first impressions.
I hope to circle back and update this post as I find new tech to learn, or as I can tick things off the list.
So here are the technologies I hope to learn more about in the coming months:
This is an infrastructure-as-code tool. I believe it’s basically trying to achieve the same thing as Canonical’s Juju (which I’m more familiar with), and I suppose, at a stretch, other recipe-making tools like Ansible, Chef, Puppet etc.
I’ve done the “Quick Start” interactive lab, which walks you through using it to create a Docker container, but that seems incredibly basic. I need to look a bit deeper into what it can actually do.
I’ve used Playwright for Python (and I’m hoping to write about it) but really it’s more of a JS library. So I should just spin it up with JS so I know how that works.
I love clean REST APIs. I know a fair bit about GraphQL, but I’ve never implemented a GraphQL interface myself. I should just quickly spin one up to see how neat it can be, and if it can avoid my primary concerns.
I’d like to know more about Grafana, and especially Loki. We use Graylog for logs at Canonical and it’s pretty cool, I understand that a fair bit, but Loki looks also interesting. We use Grafana already, but I don’t understand it as much as I’d like to.
Rust has really matured as a language. It’s now officially in the Linux kernel, and is used is more and more places. I have close friends raving about it.
I really don’t even know the basic syntax, so I’d really like to learn it, at last to the point of knowing how to install the core tooling and write and compile a basic script.
Helm is a configuration manager for Kubernetes. At Canonical, we wrote our own configuration manager, which suited our purposes pretty well, but Helm is definitely the market leader. I should know how it works to see if we can get any ideas from it.
Ruby is not especially new. I think it’s fair to say it’s somewhat in decline against Python, which we use heavily. But it’s still very relevant and it should be very easy for me to pick up the basics. It feels like a big hole in my knowledge.
AWS, Azure, GCP
At Canonical we host and manage our own private OpenStack cloud (because we also sell these to enterprise clients). This has been fantastic for learning many things about how a cloud platform is set up in general. However, I have no idea how this experience actually compares to using AWS, Azure or GCP in anger. I should really get to the point of running a basic website on each of these platforms so I can understand the landscape of their Kubernetes, CDN, Serverless etc. services.
Firebase and real-time databases
I believe Firebase offers a slick way to quickly add a data layer to an application, but I’ve never used it. I should use it in an application to understand what’s involved.
RabbitMQ and Kafka
I’ve never had the opportunity to use message queues or brokers, but I think they’re very often essential in complex systems. So I’d like to learn more about how these two work specifically, so I could make use of them in future architectures.
Mastodon and the Fediverse are definitely in the ascendency. I’d like to understand the protocol and the very basics of how to build on top of it.
I don’t think any of us can afford to ignore the evolving space of Large Language Models. ChatGPT is likely to impact anyone in the knowledge economy in one way or another. I’m not exactly sure yet what there is to learn here. I suppose it’s probably more about understanding how LLMs work so we can understand what they are and aren’t capable of (staying far, far away from the deluded “general AI” narratives).
Apache spark, pyspark
“Unified engine for large-scale data analytics”. This sounds like it could be interesting. If it’s in Python, how hard can it be?
I’m not sure how far I want to go down this path specifically, but Tensorflow certainly has a lot of buzz. I’d like to know the rough shape of it at least.
I’ve only heard this mentioned in a few places, but where it is mentioned people seem to love it. It’s another new language, mainly talked about as a Java replacement. I’d like to learn the very basics so I can see if there are any new interesting programming structures or paradigms.
These are technologies that are in the news a lot, but which I’m not interested in learning at this point:
Blockchain and cryptocurrencies definitely dominate the news cycle. I know a lot about this space already, even though I don’t know how to write any of the tech. Blockchain itself might have a future in some niche that I’m not currently aware of, but I believe cryptocurrency has had its day, and will only decline from here.