Hi Daniel,
This article is not up-to-date at all. It was written on the December
15, 2020, i.e. 3 days before the release 2.0 of Airflow!
See:
https://airflow.apache.org/docs/apache-airflow/stable/release_notes.html#airflow-2-0-0-2020-12-18
And Airflow 2.0 solves most of the yellow and red boxes from the table
you mention.
As example, please refer to the following articles (but there are many
others):
* Low latency scheduling:
https://www.astronomer.io/blog/airflow-2-scheduler/
* Event-driven workflows: one native solution is /Dataset /feature:
https://airflow.apache.org/docs/apache-airflow/stable/authoring-and-scheduling/datasets.html
* Parameterized workflows: https://docs.astronomer.io/learn/airflow-params
* Native APIs:
https://airflow.apache.org/docs/apache-airflow/stable/stable-rest-api-ref.html
I don't know how Argo evolved in the meantime, I don't know your
requirements too, but I'm pretty sure that the latest versions of
Airflow can be suitable for your use cases.
Cheers,
Hervé
Le 26/09/2023 à 13:00, Daniel Boeckenhoff a écrit :
Dear all,
I am currently investigating if airflow is the rigth solution for our
problem.
I found
https://medium.com/arthur-engineering/picking-a-kubernetes-orchestrator-airflow-argo-and-prefect-83539ecc69b
and realized it was written by an argo developer.
I would like to get your opinion on the first table in that article.
Is it biased due to its author, is it still up-to-date, are relevant
categories not compared in order to let argo shine, ...?
I appreciate all comments.
Best,
Daniel
