April 12, 2021•253 words
I'm getting more and more familiar with GCP and its capabilites as I deploy applications through things like Cloud Run now. This has been sort of a gateway into me wanting to learn about the underlying infrastructure behind it, which has in turn led me to start learning about Kubernetes setups.
I think one difficult thing about all of these options is that there are so many, that you have to really understand all of them in order to make a decision on which one you should be using.
I think if all you care about is saying, give me this CPU size in this region and serve this Image then Cloud Run is great.
I suppose the only real reason I might want to go through the extra work of managing my own kubernetes clusters would be if I wanted to make it a bit more platform agnostic and not be tied into one provider.
I think I might take one of my Cloud Run jobs and deploy a container to a Kubernetes cluster so I get a bit more end-to-end experience with it vs just sort of checking logs of pods that the infra team has procured and set up on their own clusters.
That's one of the things about data engineering; it's difficult sometimes to tell where infrastructure, devops, and data eng should differentiate and overlap. We're working through it live at work since our structure is sort of maturing over time. So far, so good.
That's all for now.