All that I've heard about Airflow is that it's intricately coupled to Python, and can be finicky to maintain as it contains a fair few moving parts.

I've previously used Argo Worfklows, which I prefer because I already have a Kubernetes environment and because it runs containers, it's totally language-agnostic which I think is a huge benefit. It also has a huge number of features for defining and controlling the workflows. Downside is that it's configuration/definition YAML's can get large and a bit messy (as YAMLs are want to do) - however, templated workflows are coming soon, which should hopefully reduce the noise.

Personally I'm reticent to use anything that re-implements its own full scheduling system instead of hooking into a pre-existing (and probably more bulletproof) one (i.e. K8s scheduler), and anything that _requires_ me to write all of my ETL/schedulable code in Python.