tnisonoff

At my company, Tyba, we build software to operate utility-scale batteries in wholesale markets (including ERCOT).

If you're interested in applying software/ML/optimization to energy markets, building distributed systems that interface with power markets, or working on problems that directly impact grid decarbonization, we're hiring engineers (in person and remote)!

Company: tyba.ai

Roles: https://tyba.ai/careers

stack traces have been much better over the past few years

Thanks for kubecfg! Happy user :)

For larger companies, I think a huge benefit of Kubernetes is the shared language for defining and operating services, as well as the well-thought-out abstractions for how these services interact.

Costs are generally less of a concern, but having one way of running, operating, and writing services allowed our dev team to move faster, share knowledge, etc.

When I worked at Asana, we created a small framework that allowed for blue-green deployments of Kubernetes Clusters (and the apps that lived on top of them) called KubeApps[0].

It worked out great for us -- upgrading Kubernetes was easy and testable, never worried about code drift, etc.

[0] https://blog.asana.com/2021/02/kubernetes-at-asana/ (Not written by me).

Interestingly, Datadog's agent was a fork of Server Density's open-source agent[1].

[1] https://en.wikipedia.org/wiki/Datadog#Technology

If the argument is Datomic can't be simpler than a relational DB because it can utilize it for persistence, then you'd have to argue that a relational DB can't be simpler than directly using a hard drive for your storage solution.