These are the sessions that were presented at Airflow Summit 2020. For other editions check the archive.

Title Speaker(s) Recording Slides

Keynote: Airflow then and now

Bolke and Maxime tell us about past on current time of Apache Airflow.
Bolke de Bruin & Maxime Beauchemin

Airflow at Société Générale : An open source orchestration solution in a banking environment

This talk covers an overview of Airflow as well as lessons learned of its implementation in a banking production environment which is Société Générale. It will be the summary of a two-year experience, a storytelling of an adventure within Société Générale in order to offer an internal cloud solution based on Airflow (AirflowaaS).

Mohammed Marragh & Alaeddine Maaoui

Scheduler as a service - Apache Airflow at EA Digital Platform

In this talk, we share the lessons learned while building a scheduler-as-a-service leveraging Apache Airflow to achieve improved stability and security for one of the largest gaming companies. The platform integrates with different data sources and meets varied SLA’s across workflows owned by multiple game studios. In particular, we present a comprehensive self-serve airflow architecture with multi-tenancy, auto-dag generation, SSO-integration with improved ease of deployment.

Nitish Victor, Preethi Ganeshan & Xiaoqin Zhu

Keynote: How large companies use Airflow for ML and ETL pipelines

In this talk, colleagues from Airbnb, Twitter and Lyft share details about how they are using Apache Airflow to power their data pipelines.

Kevin Yang, Dan Davydov & Tao Feng

Data DAGs with lineage for fun and for profit

Let’s be honest about it. Many of us don’t consider data lineage to be cool. But what if lineage would allow you to write less boilerplate and less code, while at the same time make your data scientists, your auditors, your management and well everyone more happy? What if you could write DAGs that mix between tasks based and data based?

Bolke de Bruin

Airflow on Kubernetes: Containerizing your workflows

At Nielsen Digital we have been moving our ETLs to containerized environments managed by Kubernetes. We have successfully transferred some of our ETLs to this environment in production. In order to do this we used the following technologies: Helm to easily deploy Airflow on to Kubernetes; Airflow’s Kubernetes Executor to take full advantage Kubernetes features; and Airflow’s Kubernetes Pod Operator in order to execute our containerized Tasks within our DAGs. To automate a lot of the deployment process we also used Terraform. Lastly, Kubernetes features were used to gain much more fine grained control of Airflows infrastructure.

Michael Hewitt

Data flow with Airflow @ PayPal

In PayPal we decided to move away from two of our enterprise schedulers, Control-M and UC4, to Airflow. As we started the journey, the first most important step that we wanted to take was to build all the mandatory API’s on the top of Airflow so that we could integrate with our Self-Service Tools. In this talk we share the challenges that we ran into while building APIs on top of Airflow and how we overcame them.
Aishwarya Sankaravadivel

Democratised data workflows at scale

Financial Times is increasing its digital revenue by allowing business people to make data-driven decisions. Providing an Airflow based platform where data engineers, data scientists, BI experts and others can run language agnostic jobs was a huge swing. One of the most successful steps in the platform’s development was building our own execution environment, allowing stakeholders to self deploy jobs without cross team dependencies on top of the unlimited scale of Kubernetes. In this talk we share how we have integrated and extended Airflow at Financial Times.

Mihail Petkov & Emil Todorov

Migrating Airflow-based Spark jobs to Kubernetes - the native way

At Nielsen Identity Engine, we use Spark to process 10’s of TBs of data. Our ETLs, orchestrated by Airflow, spin-up AWS EMR clusters with thousands of nodes per day. In this talk, we’ll guide you through migrating Spark workloads to Kubernetes with minimal changes to Airflow DAGs, using the open-sourced GCP Spark-on-K8s operator and the native integration we recently contributed to the Airflow project.
Roi Teveth & Itai Yaffe

Keynote: Future of Airflow

A team of core committers explain what is coming to Airflow 2.0.
Jarek Potiuk, Kaxil Naik, Tomasz Urbaszek, Ash Berlin-Taylor, Kamil Bregula & Daniel Imberman

Run Airflow DAGs in a secure way

In the contemporary world security is important more than ever - Airflow installations are no exception. Google Cloud Platform and Cloud Composer offer useful security options for running your DAGs and tasks in a way so you effectively can manage a risk of data exfiltration and access to the system is limited.

Rafal Biegacz

Airflow as the next gen of workflow system at Pinterest

At Pinterest, our current workflow system, called pinball, has served the data pipeline orchestration demands well for years. However, with the rapid increasing execution demand the system started to expose scalability and performance issues. Therefore we decided to look for a new solution to better address the issues and serve the workflow scheduling demand, and we chose Airflow as our next generation of workflow. In this talk we discuss how we made the decision to on board to Apache Airflow, and beyond the out-of-box features and experience what improvements we made to better support the business need at Pinterest.
Yulei Li, Dinghang Yu & Ace Haidrey

Keynote: Making Airflow a sustainable project through D&I

Gris Cuevas shares some statistics about the state of D&I at the Apache Software Foundation and also the initiative the foundation is taking to make projects more diverse and inclusive. Then, Aizhamal shares her own journey on becoming an open source contributor, and dives into project specific initiatives that help Apache Airflow to be one of the most sustainable projects in open source.
aizhamal-nurmamat & Griselda Cuevas

Improving Airflow's user experience

Astronomer is focused on improving Airflow’s user experience through the entire lifecycle — from authoring + testing DAGs, to building containers and deploying the DAGs, to running and monitoring both the DAGs and the infrastructure that they are operating within — with an eye towards increased security and governance as well. In this talk we walk you through some current UX challenges, an overview of how the Astronomer platform addresses the major challenges, and also provide sneak peek of the things that we’re working on in the coming months to improve Airflow’s user experience.

Ry Walker, Maxime Beauchemin & Viraj Parekh

Airflow CI/CD: Github to Cloud Composer (safely)

Deploying bad DAGs to your Airflow environment can wreak havoc. This talk provides an opinionated take on a mono repo structure for GCP data pipelines leveraging BigQuery, Dataflow and a series of CI tests for validating your Airflow DAGs before deploying them to Cloud Composer.

Jacob Ferriero

Advanced Apache Superset for Data Engineers

Superset is the leading open source data exploration and visualization platform. In this talk, we’ll be presenting Superset with a focus on advanced topics that are most relevant to Data Engineers. The presentation will be largely a live demo of the product, with a deeper dive into advanced topics for Data Engineers.

Maxime Beauchemin

Teaching an old DAG new tricks

Scribd is migrating its data pipeline from an in house system to Airflow. It’s a one big giant data pipeline consisting of more than 1,500 tasks. In this talk, I would like to share couple best practices on setting up a cloud native Airflow deployment in AWS. For those who are interested in migrating a non-trivial data pipeline to Airflow, I will also share how Scribd plans and executes the migration.

QP Hou

Ask me anything with Airflow members

We will host an ‘ask me anything’ with a group of Airflow committers & PMC members.
Jarek Potiuk & Kaxil Naik

Demo: Reducing the lines, a visual DAG editor

In this talk I will introduce a DAG authoring and editing tool for Airflow that we have built. Installed as a plugin, this tool allows users to author DAGs compose existing operators and hooks with virtually no Python experience. We walk through a demo of DAG authorship and deployment, and spend time reviewing the underlying open-source standards used and the general approach that was taken to develop the code.

Traey Hatch

AIP-31: Airflow functional DAG definition

Airflow does not currently have an explicit way to declare messages passed between tasks in a DAG. XCom are available but are hidden in execution functions inside the operator. AIP-31 proposes a way to make this message passing explicit in the DAG file and make it easier to reason about your DAG behaviour.

Gerard Casas Saez

Using Airflow to speed up development of data intensive tools

In this talk we review how Airflow helped create a tool to detect data anomalies. Leveraging Airflow for process management, database interoperability, and authentication created an easy path forward to achieve scale, decrease the development time and pass security audits. While Airflow is generally looked at as a solution to manage data pipelines, integrating tools with Airflow can also speed up development of those tools.

Blaine Elliot

Autonomous driving with Airflow

This talk describes how Airflow is utilized in an Autonomous driving project, originating from Munich - Germany. We describe the Airflow setup, what challenges we encountered and how we maneuvered to achieve a distributed and highly scalable Airflow setup.

Amr Noureldin & Michal Dura

From cron to Airflow on Kubernetes: A startup story

Learn how Devoted Health went from cron jobs to Airflow deployment Kubernetes using a combination of open source and internal tooling.

Adam Boscarino

Airflow in Airbnb

Go over the yesterday, today and tomorrow for Airflow in Airbnb. Share our learnings and vision in Airflow core and around Airflow in its eco system.

Kevin Yang, Ping Zhang, Yingbo Wang, Cong Zhu & Conor Camp

Pipelines on pipelines: Agile CI/CD workflows for Airflow DAGs

How do you create fast and painless delivery of new DAGs into production? When running Airflow at scale, it becomes a big challenge to manage the full lifecycle around your pipelines; making sure that DAGs are easy to develop, test, and ship into prod. In this talk, we will cover our suggested approach to building a proper CI/CD cycle that ensures the quality and fast delivery of production pipelines.

Victor Shafran

Production Docker image for Apache Airflow

This talk will guide you trough internals of the official Production Docker Image of Airflow. It will show you the foreseen use cases for it and how to use it in conjunction with the Official Helm Chart to make your own deployments.
Jarek Potiuk

Airflow as an elastic ETL tool

In search of a better, modern, simplistic method of managing ETL’s processes and merging them with various AI and ML tasks, we landed on Airflow. We envisioned a new user friendly interface that can leverage dynamic DAG’s and reusable components to build an ETL tool that requires virtually no training.

Hendrik Kleine & Vicente Ruben del Pino Ruiz

How do we reason about the reliability of our data pipeline in Wrike

In this talk we will share some of the lessons we have learned after using Airflow for a couple of years and growing from 2 users to 8 teams. We cover: establishing a reliable review process on AirFlow, managing multiple Airflow configurations, data versioning.
Alexander Eliseev

Achieving Airflow observability with Databand

While Airflow is a central product for data engineering teams, it’s usually one piece of a bigger puzzle. The vast majority of teams use Airflow in combination with other tools like Spark, Snowflake, and BigQuery. Making sure pipelines are reliable, detecting issues that lead to SLA misses, and identifying data quality problems requires deep visibility into DAGs and data flows. Join this session to learn how Databand’s observability system makes it easy to monitor your end-to-end pipeline health and quickly remediate issues.

Josh Benamram

From S3 to BigQuery - How a first-time Airflow user successfully implemented a data pipeline

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.

Leah Cole

Building reuseable and trustworthy ELT pipelines (A templated approach)

To improve automation of data pipelines, I propose a universal approach to ELT pipeline that optimizes for data integrity, extensibility, and speed to delivery. The workflow is built using open source tools and standards like Apache Airflow, Singer, Great Expectations, and DBT.

Nehil Jain

Testing Airflow workflows - ensuring your DAGs work before going into production

How do you ensure your workflows work before deploying to production? In this talk I’ll go over various ways to assure your code works as intended - both on a task and a DAG level.

Bas Harenslak

Adding an executor to Airflow: A contributor overflow exception

Engaging with a new community is a common experience in OSS development. There are usually expectations held by the project about the contributor’s exposure to the community, and by the contributor about interactions with the community. When these expectations are misaligned, the process is strained. In this talk Vanessa discusses a real life experience that required communication, persistence, and patience to ultimately lead to a positive outcome.

Vanessa Sochat

Migration to Airflow backport providers

In this talk Anita showcases how to use the newly released Airflow Backport Providers.

Anita Fronczak

From Zero to Airflow: bootstrapping a ML platform

At Bluevine we use Airflow to drive our ML platform. In this talk, Noam presents the challenges and gains we had at transitioning from a single server running Python scripts with cron to a full blown Airflow setup. This includes: supporting multiple Python versions, event driven DAGs, performance issues and more!

Noam Elfanbaum

Airflow the perfect match in our analytics pipeline

For three years we at LOVOO, a market-leading dating app, have been using the Google Cloud managed version of Airflow, a product we’ve been familiar with since its Alpha release. We took a calculated risk and integrated the Alpha into our product, and, luckily, it was a match. Since then, we have been leveraging this software to build out not only our data pipeline, but also boost the way we do analytics and BI.

Sergio Camilo Fandiño Hernandez

Data engineering hierarchy of needs

Data Infrastructures look differently between small, mid, and large sized companies. Yet, most content out there is for large and sophisticated systems. And almost none of it is on migrating a legacy, on-prem, databases over to the cloud. In order to better explain the evolving needs of data engineering organizations, we will review the hierarchy of needs for data engineering.

Angel Daz

What open source taught us about business

This talk shares Polidea’s journey from mobile app development studio to an OSS oriented business partner. We will tell you our story towards code leadership throughout the years. We are also going to share the challenges and practical insights into managing open source projects in our company. After this talk, you will know how we approached combining open source, business and team management not forgetting about a human aspect.

Karolina Rosol & Maciej Oczko

Effective Cross-DAG dependency

Cross-DAG dependency may reduce cohesion in data pipelines and, without having an explicit solution in Airflow or in a third-party plugin, those pipelines tend to become complex to handle. That is the reason we, at QuintoAndar, have created an intermediate DAG to handle relationships across data pipelines called Mediator, in order for them to be scalable and maintainable by any team.

Rafael Ribaldo & Lucas Fonseca

Airflow: A beast character in the gaming world

Being a pioneer for the past 25 years, SONY PlayStation has played a vital role in the Interactive Gaming Industry. Over 100+ million monthly active users, 100+ million PS-4 console sales along with thousands of game development partners across the globe, big-data problem is quite inevitable. This presentation talks about how we scaled Airflow horizontally which has helped us building a stable, scalable and optimal data processing infrastructure powered by Apache Spark, AWS ECS, EC2 and Docker.

Naresh Yegireddi & Patricio Garza

Machine Learning with Apache Airflow

This talk discusses how to build an Airflow based data platform that can take advantage of popular ML tools (Jupyter, Tensorflow, Spark) while creating an easy-to-manage/monitor

Daniel Imberman

Achieving Airflow Observability

Identify issues in a fraction of the time and streamline root cause analysis for your DAGs. Airflow is the leading orchestration platform for data engineers. But when running Airflow at production scale, many teams have bigger needs for monitoring jobs, creating the right level of alerting, tracking problems in data, and finding the root cause of errors. In this talk we will cover our suggested approach to gaining Airflow observability so that you have the visibility you need to be productive.

Evgeny Shulman