With a small amount of Cloud Build automation and the use of GitHub version control, your Airflow DAGs will always be tested and in sync no matter who is working on them. Leah will walk you through a sample CICD workflow for keeping your Airflow DAGs tested and in sync between environments and teammates.

This workshop is sold out Hands on workshop showing how easy it is to deploy Airflow in a public Cloud. Workshop consists of 3 parts: Setting up Airflow environment and CI/CD for DAG deployment Authoring a DAG Troubleshoot Airflow DAG/Task execution failures This workshop will be based on Cloud Composer ( https://cloud.google.com/composer ) This workshop is mostly targeted at Airflow newbies and users who would like to learn more about Cloud Composer and how to develop DAGs using Google Cloud Platform services like BigQuery, Vertex AI, Dataflow.

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.

Rachael, a new Airflow contributor, and Leah, an experienced Airflow contributor, share the story of Rachael’s first contribution, highlighting the importance of contributions from new users and the positive impact that non-code contributions have in an open source community.

BigQuery is GCP’s serverless, highly scalable and cost-effective cloud data warehouse that can analyze petabytes of data at super fast speeds. Amazon S3 is one of the oldest and most popular cloud storage offerings. Folks with data in S3 often want to use BigQuery to gain insights into their data. Using Apache Airflow, they can build pipelines to seamlessly orchestrate that connection. In this talk, Leah walks through how they created an easily configurable pipeline to extract data. When a team at work mentioned wanting to set up a repeatable process for migrating data stored in S3 to BigQuery, Leah knew using Cloud Composer (GCP-hosted Airflow) was the right tool for the job, but she didn’t have much experience with the proprietary file types the data used. Luckily, one of her colleagues did have experience with that proprietary file type, though they hadn’t worked with Airflow. Leah and her colleague teamed up to build a reusable, easily configurable solution for the team. She will walk you through their problem, the solution, and the process they took for coming to that solution, highlighting resources that were especially useful to a first-time Airflow user.