OpenLineage is an open standard for lineage data collection, integrated into the Airflow codebase, facilitating lineage collection across providers like Google, Amazon, and more. Atlan Data Catalog is a 3rd generation active metadata platform that is a single source of trust unifying cataloging, data discovery, lineage, and governance experience. We will demonstrate what OpenLineage is and how, with minimal and intuitive setup across Airlfow and Atlan, it presents unified workflows view, efficient cross-platform lineage collection, including column level, in various technologies (Python, Spark, dbt, SQL etc.) and clouds (AWS, Azure, GCP, etc.) - all orchestrated by Airflow. This integration enables further use case unlocks on automated metadata management by making the operational pipelines dataset-aware for self-service exploration. It also will demonstrate real world challenges and resolutions for lineage consumers in improving audit and compliance accuracy through column-level lineage traceability across the data estate. The talk will also briefly overview the most recent OpenLineage developments and planned future enhancements.

NCR Voyix Retail Analytics AI team offers ML products for retailers while embracing Airflow as its MLOps Platform. As the team is small and there have been twice as many data scientists as engineers, we encountered challenges in making Airflow accessible to the scientists: As they come from diverse programming backgrounds, we needed an architecture enabling them to develop production-ready ML workflows without prior knowledge of Airflow. Due to dynamic product demands, we had to implement a mechanism to interchange Airflow operators effortlessly. As workflows serve multiple customers, they should be easily configurable and simultaneously deployable. We came up with the following architecture to deal with the above: Enabling our data scientists to formulate ML workflows as structured Python files. Seamlessly converting the workflows into Airflow DAGs while aggregating their steps to be executed on different Airflow operators. Deploying DAGs via CI/CD’s UI to the DAGs folder for all customers while considering definitions for each in their configuration files. In this session, we will cover Airflow’s evolution in our team and review the concepts of our architecture.

DAGify is a highly extensible, template driven, enterprise scheduler migration accelerator that helps organizations speed up their migration to Apache Airflow. While DAGify does not claim to migrate 100% of existing scheduler functionality it aims to heavily reduce the manual effort it takes for developers to convert their enterprise scheduler formats into Python Native Airflow DAGs. DAGify is an open source tool under Apache 2.0 license and available on Github ( https://github.com/GoogleCloudPlatform/dagify) . In this session we will introduce DAGify, its use cases and demo its functionality by converting Control-M XML files to Airflow DAGs. Additionally we will highlight DAGify’s “no-code” extensibility by creating custom conversion templates to map Control-M functionality to Airflow operators.

At Wix more often than not business analysts build workflows themselves to avoid data engineers being a bottleneck. But how do you enable them to create SQL ETLs starting when dependencies are ready and sending emails or refreshing Tableau reports when the work is done? One simple answer may be to use Airflow. The problem is every BA cannot be expected to know Python and Git so well that they will create thousands of DAGs easily. To bridge this gap we have built a web-based IDE, called Quix, that allows simple notebook-like development of Trino SQL workflows and converts them to Airflow DAGs when a user hits the “schedule” button. During the talk we will go through the problems of building a reliable and extendable DAG generating tool, why we preferred Airflow over Apache Oozie and also tricks (sharding, HA-mode, etc) allowing Airflow to run 8000 active DAGs on a single cluster in k8s.

Up until a few years ago, teams at Uber used multiple data workflow systems, with some based on open source projects such as Apache Oozie, Apache Airflow, and Jenkins while others were custom built solutions written in Python and Clojure. Every user who needed to move data around had to learn about and choose from these systems, depending on the specific task they needed to accomplish. Each system required additional maintenance and operational burdens to keep it running, troubleshoot issues, fix bugs, and educate users. After this evaluation, and with the goal in mind of converging on a single workflow system capable of supporting Uber’s scale, we settled on an Airflow-based system. The Airflow-based DSL provided the best trade-off of flexibility, expressiveness, and ease of use while being accessible for our broad range of users, which includes data scientists, developers, machine learning experts, and operations employees. This talk will focus on scaling Airflow to Uber’s scale and providing a no-code seamless user experience

Artificial Intelligence is reshaping the landscape of software development. In this talk, we’ll explore the latest AI breakthroughs improving LLM capabilities for software development use cases. We’ll discuss work and ideas in the field related to Airflow, particularly around model capabilities related to Python, DSLs, and low-resource languages.

DAG Authors, while constructing DAGs, generally use native libraries provided by Airflow in conjunction with python libraries available over public PyPI repositories. But sometimes, DAG authors need to construct DAG using libraries that are either in-house or not available over public PyPI repositories. This poses a serious challenge for users who want to run their custom code with Airflow DAGs, particularly when Airflow is deployed in a cloud-native fashion. Traditionally, these packages are baked in Airflow Docker images. This won’t work post deployment and is super impractical if your library is under development. We propose a solution that creates a dedicated Airflow global python environment that dynamically generates the requirements, establishes a version-compatible pyenv adhering to Airflow’s policies, and manages custom pip repository authentication seamlessly. Importantly, the service executes these steps in a fail-safe manner, not compromising core components. Join us as we discuss the solution to this common problem, touching upon the design, and seeing the solution in action. We also candidly discuss some challenges, and the shortcomings of the proposed solution.

Airflow’s power comes from its vast ecosystem, but securing this intricate web requires a united front. This talk unveils a groundbreaking collaborative effort between the Python Software Foundation (PSF), the Apache Software Foundation (ASF), the Airflow Project Management Committee (PMC), and Alpha-Omega Fund - aimed at securing not only Airflow, but the whole ecosystem. We’ll explore this new project dedicated to improving security across the Airflow landscape.

Jupyter Notebooks are widely used by data scientists and engineers to prototype and experiment with data. However these engineers are often required to work with other data or platform engineers to productionize these experiments due to the complexity in navigating infrastructure and systems. In this talk, we will deep dive into this PR https://github.com/apache/airflow/pull/34840 and share how airflow can be leveraged as a platform to execute notebook pipelines (python, scala or spark) in dynamic environments like Kubernetes for various heterogeneous use cases. We will demonstrate how data scientists can use a Jupyter extension to easily build and manage such pipelines which are executed using Airflow streamlining data science workflow development and supercharging productivity

As organizations grow, the task of creating and managing Airflow DAGs efficiently becomes a challenge. In this talk, we will delve into innovative approaches to streamlining Airflow DAG creation using YAML. By leveraging YAML configuration, we allow users to dynamically generate Airflow DAGs without requiring Python expertise or deep knowledge of Airflow primitives. We will showcase the significant benefits of this approach, including eliminating duplicate configurations, simplifying DAG management for a large group of workflows, and ultimately enhancing productivity within large organizations. Join us to learn practical strategies to optimize workflow orchestration, reduce development overhead, and facilitate seamless collaboration across teams.