golang-developer-roadmap VS data-engineer-roadmap

I have found this road map kind of summarized frameworks and tools but I am not sure if it's up to date https://github.com/Alikhll/golang-developer-roadmap

Golang tutorial: Table Of Contentshttps://golangbot.com/learn-golang-series/https://quii.gitbook.io/learn-go-with-tests/ - Learn Go by testshttps://gobyexample.com/https://tour.golang.org/https://github.com/Alikhll/golang-developer-roadmap

Hey guys Im really new to golang.Learning some basics and willing to switch my whole career to golang. Im a junior frontend developer rn.And starting backend with Go.I was looking for some good structered roadmap and found this https://github.com/Alikhll/golang-developer-roadmap. Its from 2020. Should I still follow it?

Roadmap: https://github.com/Alikhll/golang-developer-roadmap/blob/master/golang-developer-roadmap.png

Check out a proposal for a Go Developer Roadmap for learning Go in 2020

Don't know if there's an official one but I'm following this roadmap Golang Roadmap

Here is a good perspective to be "industry ready". Remember you shouldn't be an expert in everything, but for a junior dev know at least the terms: https://github.com/Alikhll/golang-developer-roadmap

In DevOps tools I've worked with GitHub + Jenkins, GitLab + k8s, and I'm now primarily working in the Argo Stack. Depending on where you're at technically, you might use something different. IaC is a ust as well, maybe some config management. Generally I've found that as a Data Engineer with a lot of infra/CICD knowledge, I generally get pigeonholed into those positions on a team, so be prepared for that. I really like this roadmap for DevOps , so you can see where your tech skills are at currently, and what you may need to learn. On top of that, you'll need to learn some data tools. Airflow + dbt is hot right now, Argo is sometimes used in MLOps, Azure Data Stack (I'm not familiar with it) seems common, and probably Spark in almost all cases. You can also checkout in visualization tools probably further down the line, I generally stick to something free when learning on my own, Superset or Google Data Studio (Might be Looker Studio now? Not sure, it's been a while). Here's a roadmap for DE too. I love these roadmaps for getting started, but don't let them distract you from exploring a path more appropriate to what you want to achieve. Generally I've found that as a Data Enigneer with a lot of infra/CICD knowledge, I generally get pigeonholed into those positions on a team

Anyways, sorry bit of a rant - I land somewhere in the middle. I would say take formal classes and resources when you can. If you have access to a free course a semester, that's incredible in my opinion. If I were in your shoes, I would follow a roadmap and see if there are courses that check off a box in that roadmap. So for example, you know you need to learn CS fundamentals - see if you can take a DSA class or something. Or take a class on databases. Or an OOP or databases class. I would take those classes if I had the opportunity just because I didn't when I was in college. No one course will check every box for sure.

I don't remember all of the name of the courses but I think this roadmap can put you in the right direction https://github.com/datastacktv/data-engineer-roadmap

You can follow this roadmap https://github.com/datastacktv/data-engineer-roadmap I have already replied some redditors with suggestions, you can read them.