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In this talk, I’ll describe how you can leverage 3 open-source standards - workflow management with Airflow, EL with Airbyte, transformation with DBT - to build your next modern data stack. I’ll explain how to configure your Airflow DAG to trigger Airbyte’s data replication jobs and DBT’s transformation one with a concrete use case.

At Near we work on TBs of Location data with close to real time modelling to generate key consumer insights and estimates for our clients across the globe. We have hundreds of country specific models deployed and managed through airflow to achieve this goal. Some of the workflows that we have deployed our schedule based, some are dynamic and some are trigger based. In this session I would be discussing some of the workflows that are being scheduled and monitored using airflow and the key benefits and also the challenges that we have faced in our production systems.

An informal and fun chat about the journey that we took and the decisions that we made in building Amazon Managed Workflows for Apache Airflow. We will talk about Our first tryst with understanding Airflow Talking to Amazon Data Engineers and how they ran workflows at scale Key design decisions and the reasons behind them Road ahead, and what we dream about for future of Apache Airflow. Open-Source tenets and commitment from the team We will leave time at the end for a short AMA/Questions.

In recent years, the bioinformatics world has seen an explosion in genomic analysis as gene sequencing technologies have become exponentially cheaper. Tests that previously would have cost tens of thousands of dollars will soon run at pennies per sequence. This glut of data has exposed a notable bottleneck in the current suite of technologies available to bioinformaticians. At Drift Biotechnologies, we use Apache Airflow to transition traditionally on-premise large scale data and deep learning workflows for bioinformatics to the cloud, with an emphasis on workflows and data from next generation sequencing technologies.

As a follow up for https://airflowsummit.org/sessions/teaching-old-dag-new-tricks/ , in this talk, we would like to share a happy ending story on how Scribd fully migrated its data platform to the cloud and Airflow 2.0. We will talk about data validation tools and task trigger customizations the team built to smooth out the transition. We will share how we completed the Airflow 2.0 migration started with an unsupported MySQL version and metrics to prove why everyone should perform the upgrade. Lastly, we will discuss how large scale backfills (10 years worth of run) are managed and automated at Scribd.

At QuintoAndar we seek automation and scalability in our data pipelines and believe that Airflow is the right tool for giving us exactly what we need. However, having all concerns mapped and tooling defined doesn’t necessarily mean success. For months we had struggled with a misconception that Airflow should act as an orchestrator and executor within a monolithic strategy. That could not be further from the truth because of the rise of scalability and performance issues, infrastructure and maintainability costs, and multi-directional impact throughout development teams. Employing Airflow, though, as an orchestration-only solution may help teams deliver value to end users in a more efficient, reliable and performant manner, where data pipelines can be executed anywhere with proper resources and optimizations. Those are the reasons we have shifted from an orchestrate-execute strategy to an orchestrate-only one, in order to leverage the full power of data pipeline management in Airflow. Straightaway the separation of data processing and pipeline coordination brought not only a finer resource tuning and better maintainability, but also a tremendous scalability on both ends.

Machine Learning models can add value and insight to many projects, but they can be challenging to put into production due to problems like lack of reproducibility, difficulty maintaining integrations, and sneaky data quality issues. Kedro, a framework for creating reproducible, maintainable, and modular data science code, and Great Expectations, a framework for data validations, are two great open-source Python tools that can address some of these problems. Both integrate seamlessly with Airflow for flexible and powerful ML pipeline orchestration. In this talk we’ll discuss how you can leverage existing Airflow provider packages to integrate these tools to create sustainable, production-ready ML models.

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

In Apache Airflow, Xcom is the default mechanism for passing data between tasks in a DAG. In practice, this has been restricted to small data elements, since the Xcom data is persisted in the Airflow metadatabase and is constrained by database and performance limitations. With the new TaskFlow API introduced in Airflow 2.0, it is seamless to pass data between tasks and the use of Xcom is invisible. However, the ability to pass data is restricted to a relatively small set of data types which can be natively converted in JSON. This tutorial describes how to go beyond these limitations by developing and deploying a Custom Xcom backend within Airflow to enable the sharing of large and varied data elements such as Pandas data frames between tasks in a data pipeline, using a cloud storage such as Google Storage or Amazon S3.

EA Games have very dynamic and federated needs on their data processing pipelines. Many individual studios within EA build and manage the data pipelines for their games iterating rapidly through game development cycles. Developer productivity around orchestrating these pipelines is as critical as providing a robust production quality orchestration service. With these in mind, we re-engineered our Airflow service ground up to cater to our large internal user base (1000s) and internet scale data processing systems (Petabytes of data). This session details the evolution of the use of Airflow at EA Digital Platform from a monolithic multi-tenant instance to an “On-Demand” system where teams and studios create their own dedicated Airflow instance with all the necessary bells-and-whistles required at the click of a button - and allows them to immediately get their data pipelines running. We also elaborate how Airflow is interwoven into a “Self Serve” model for ETL pipelines within our teams with the objective of truely democratizing data across our games.

Wise (previously TransferWise) is a London-based fin-tech company. We build a better way of sending money internationally. At Wise we make great use of Airflow. More than 100 data scientists, analysts and engineers use Airflow every day to generate reports, prepare data, (re)train machine learning models and monitor services. My name is Alexandra, I’m a Machine Learning Engineer at Wise. Our team is responsible for building and maintaining Wise’s Airflow instances. In this presentation I would like to talk about three main things, our current setup, our challenges and our future plans with Airflow. We are currently transitioning from a single centralised Airflow instance into many segregated instances to increase reliability and limit access. We’ve learned a lot throughout this journey and looking to share these learnings with a wider audience.

Considering that the role of Analytics Engineering has emerged in the last few years within data and analytics teams, it is important for me to highlight what role an Analytics engineer has and how the Dos and Don’ts from my perspective can contribute to a team and boost their day-to-day work with the help of Airflow.

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

We are witnessing a rapid growth in the number of mission-critical data pipelines that leaders of data products are responsible for. “Are your data pipelines healthy?” This question was posed to more than 200 leaders of data products from various industries. The answers ranged from “unfortunately, no” to “they are mostly fine, but I am always afraid that something or the other will cause a pipeline to break”. This talk presents the concept of Pipeline HealthCheck (PHC) which enables leaders of data products to have high confidence in the correctness, performance, and cost efficiency of their data pipelines. More importantly, PHC enables leaders of data products as well as their development and operations teams to have high confidence in their ability to quickly detect, troubleshoot, and fix problems that make data pipelines unhealthy. The talk also includes a demo of how PHC helps handle common problems in data pipelines like incorrect results, missing SLAs, and overshooting cost budgets.

Apache Superset is a modern, open-source data exploration & visualization platform originally created by Maxime Beauchemin. In this talk, I will showcase advanced technical Superset features like the rich Superset API, how to version control dashboards using Github, embedding Superset charts in other applications, and more. This talk will be technical and hands-on, and I will share all code examples I use so you can play with them yourself afterwards!

Multi-tenant Airflow instances can help save costs for an organization. This talk will walk through how we dynamically assigned roles to users based on groups in Active Directory so that teams would have access to DAGs they created in the UI on our multi-tenant Airflow instance. We created our own custom AirflowSecurityManager class in order to achieve this that ultimately ties LDAP and RBAC together.

Engineering teams leverage the factory coding pattern to write easy-to-read and repeatable code. In this talk, we’ll outline how data engineering teams can do the same with Airflow by separating DAG declarations from business logic, abstracting task declarations from task dependencies, and creating a code architecture that is simple to understand for new team members. This approach will set analytics teams up for success as team and Airflow DAG sizes grow exponentially.

Using Airflow as our scheduling framework, we ETL data generated by tens of millions of transactions every day to build the backbone for our reports, dashboards, and training data for our machine learning models. There are over 500 (and growing) such ingested and aggregated tables owned by multiple teams that contain intricate dependencies between one another. Given this level of complexity, it can become extremely cumbersome to coordinate backfills for any given table, when also taking into account all its downstream dependencies, aggregation intervals, and data availability. This talk will focus on how we customized and extended Airflow at Adyen to streamline our backfilling operations. This allows us to prevent mistakes and enable our product teams to keep launching fast and iterating.

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.