In this talk Jarek and Kaxil will talk about official, community support for running Airflow in the Kubernetes environment. The full support for Kubernetes deployments was developed by the community for quite a while and in the past users of Airflow had to rely on 3rd-party images and helm-charts to run Airflow on Kubernetes. Over the last year community members made an enormous effort to provide robust, simple and versatile support for those deployments that would respond to all kinds of Airflow users. Starting from official container image, through quick-start docker-compose configuration, culminating in April with release of the official Helm Chart for Airflow. This talk is aimed for Airflow users who would like to make use of all the effort. The users will learn how to: Extend or customize Airflow Official Docker Image to adapt it to their needs Run quickstart docker-compose environment where they can quickly verify their images Configure and deploy Airflow on Kubernetes using the Official Airflow Helm chart

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

After performing several experiments with Airflow, we reached the best architectural design for processing text medical records in scale. Our hybrid solution uses Kubernetes, Apache Airflow, Apache Livy, and Apache cTAKES. Using Kubernetes’ containers has the benefit of having a consistent, portable, and isolated environment for each component of the pipeline. With Apache Livy, you can run tasks in a Spark Cluster at scale. Additionally, Apache cTAKES helps with the extraction of information from electronic medical records clinical free-text by using natural language processing techniques to identify codable entities, temporal events, properties, and relations.

This presentation will detail how Elyra creates Jupyter Notebook, Python and R script- based pipelines without having to leave your web browser. The goal of using Elyra is to help construct data pipelines by surfacing concepts and patterns common in pipeline construction into a familiar, easy to navigate interface for Data Scientists and Engineers so they can create pipelines on their own. In Elyra’s Pipeline Editor UI, portions of Apache Airflow’s domain language are surfaced to the user and either made transparent or understandable through the use of tooltips or helpful notes in the proper context during pipeline construction. With these features, Elyra can rapidly prototype data workflows without the need to know or write any pipeline code. Lastly, we will look at what features we have planned on our roadmap for Airflow, including more robust Kubernetes integration and support for runtime specific components/operators. Project Home: https://github.com/elyra-ai/elyra

Last year, we were able to share why we have selected Airflow to be our next generation workflow system. This year, we will dive into the journey of migrating over 3000+ workflows and 45000+ tasks to Airflow. We will discuss the infrastructure additions to support such loads, the partitioning and prioritization of different workflow tiers defined in house, the migration tooling we built to get users to onboard, the translation layers between our old DSLs and the new, our internal k8s executor to leverage Pinterest’s kubernetes fleet, and more. We want to share the challenges both technically and usability wise to get such large migrations over the course of a year, and how we overcame it to successfully migrate 100% of the workflows to our inhouse workflow platform branded Spinner.