Apache Airflow is known to be a great orchestration tool that enables use cases that would not be possible otherwise. One of the great features that Airflow has is the possibility to “glue” together totally separate services to establish bigger functionalities. In this talk you will learn about various Airflow usages that let Airflow users to automate their critical company processes and even establish businesses. The examples provided will be based on Airflow used in the context of Cloud Composer which is a managed service to provision and manage Airflow instances.

Autoscaling in Airflow - what we learnt based on Cloud Composer case. We would like to present how we approach the autoscaling problem for Airflow running in Kubernetes in Cloud Composer: how we calculate our autoscaling metric, what problem we had for scaling down and how did we solve it. Also we share an ideas on what and how we could improve the current solution

As part of my role at Google, maintaining samples for Cloud Composer, hosted managed Airflow, is crucial. It’s not feasible for me to try out every sample every day to check that it’s working. So, how do I do it? Automation! While I won’t let the robots touch everything, they let me know when it’s time to pay attention. Here’s how: Step 0: An update for the operators is released Step 1: A GitHub bot called Renovate Bot opens up a PR to a special requirements file to make this update Step 2: Cloud build runs unit tests to make sure none of my DAGs immediately break Step 3: PR is approved and merged to main Step 4: Cloud Build updates my dev environment Step 5: I look at my DAGs in dev to make sure all is well. If there is a problem, I need to resolve it manually and revert my requirements file. Step 6: I manually update my prod PyPI packages I’ll discuss what automation tools I choose to use and why, and the places where I intentionally leave manual steps to ensure proper oversight.