One Q - it seems to me that another possible solve (and probably how the big guys tend to do it) is to use a dataflow engine like Spark/Flink. Did you compare a managed platform like Google Dataproc? They also have serverless if you don’t want a heavy managed cluster, which might make this approach more viable for non-huge companies that wouldn’t utilize a min-spec cluster. (When I last evaluated this they didn’t have serverless which was a dealbreaker for my small scale).
This sounds like an issue not with Airflow but with integration.
DAGs can be published to S3 for cutting down on like half of these dependencies. And the nice thing about MWAA is log & stats publishing over cloudwatch, which should flow into any existing amazon integrated tooling.
For our team setting up terraform for iam & mwaa, some deploy pipelines to s3, and connecting some config bits to wire up splunk logs / monitoring pieces was not that much work. Initiating a separated vendor relationship & pricing out data ingress/egress costs would blow that work out of the water but maybe it’s a difference in company size/placement.
In fact, I did end up doing all those things, but we opted for Dagster Cloud, because of their focus on improving developer efficiency. Their team provided pre-built Github actions for CI/CD and recently introduced PR-specific branch deployments, which has been amazing. They're moving towards serverless execution, built-in ECR repositories, managed secrets. Prefect and Astronomer I expect are moving in this direction, too, but I liked the Dagster project's energy quite a bit.
As I've waded into the MLOps world as well, it just keeps looking like every platform basically devolves into : an orchestrator that provisions compute resources and logs metadata into an opinionated data model. Catalog tools like Atlan are metadata sinks that are trying to build out orchestration/workflow capabilities. dbt Cloud of course is just an orchestrator for a specific type of data product that is aiming to operationalize metadata with its metrics layer.
Orchestration + a metadata data model is a common denominator here, and I think the fact that Airflow is so inevitable has made it really hard for people to imagine the category as anything other than a scheduler, but perhaps some of these new companies can break new ground.