As teams scale their Airflow workflows, a common question is: “My DAG has 5,000 tasks—how long will it take to run in Airflow?” Beyond execution time, users often face challenges with dynamically generated DAGs, such as: Delayed visualization in the Airflow UI after deployment. High resource consumption, leading to Kubernetes pod evictions and out-of-memory errors. While estimating the resource utilization in a distributed data platform is complex, benchmarking can provide crucial insights. In this talk, we’ll share our approach to benchmarking dynamically generated DAGs with Astronomer Cosmos ( https://github.com/astronomer/astronomer-cosmos) , covering: Designing representative and extensible baseline tests. Setting up an isolated, distributed infrastructure for benchmarking. Running reproducible performance tests. Measuring DAG run times and task throughput. Evaluating CPU & memory consumption to optimize deployments. By the end of this session, you will have practical benchmarks and strategies for making informed decisions about evaluating the performance of DAGs in Airflow.

Efficiently handling long-running workflows is crucial for scaling modern data pipelines. Apache Airflow’s deferrable operators help offload tasks during idle periods — freeing worker slots while tracking progress. This session explores how Cosmos 1.9 ( https://github.com/astronomer/astronomer-cosmos ) integrates Airflow’s deferrable capabilities to enhance orchestrating dbt ( https://github.com/dbt-labs/dbt-core ) in production, with insights from recent contributions that introduced this functionality. Key takeaways: Deferrable Operators: How they work and why they’re ideal for long-running dbt tasks. Integrating with Cosmos: Refactoring and enhancements to enable deferrable behaviour across platforms. Performance Gains: Resource savings and task throughput improvements from deferrable execution. Challenges & Future Enhancements: Lessons learned, compatibility, and ideas for broader support. Whether orchestrating dbt models on a cloud warehouse or managing large-scale transformations, this session offers practical strategies to reduce resource contention and boost pipeline performance.

As organizations scale their data infrastructure, Apache Airflow becomes a mission-critical component for orchestrating workflows efficiently. But scaling Airflow successfully isn’t just about running pipelines—it’s about building a Center of Excellence (CoE) that empowers teams with the right strategy, best practices, and long-term enablement. Join Jon Leek and Michelle Winters as they share their experiences helping customers design and implement Airflow Centers of Excellence. They’ll walk through real-world challenges, best practices, and the structured approach Astronomer takes to ensure teams have the right plan, resources, and support to succeed. Whether you’re just starting with Airflow or looking to optimize and scale your workflows, this session will give you a proven framework to build a sustainable Airflow Center of Excellence within your organization. 🚀

We’re excited to offer Airflow Summit 2025 attendees an exclusive opportunity to earn their DAG Authoring certification in person, now updated to include all the latest Airflow 3.0 features. This certification workshop comes at no additional cost to summit attendees. The DAG Authoring for Apache Airflow certification validates your expertise in advanced Airflow concepts and demonstrates your ability to build production-grade data pipelines. It covers TaskFlow API, Dynamic task mapping, Templating, Asset-driven scheduling, Best practices for production DAGs, and new Airflow 3.0 features and optimizations. The certification session includes: 20-minute preparation period with expert guidance Live Q&A session with Marc Lamberti from Astronomer 60-minute examination period Real-time results and immediate feedback To prepare for the Airflow Certification, visit the Astronomer Academy ( https://academy.astronomer.io/page/astronomer-certification) .

Airflow 3.0 is the most significant release in the project’s history, and brings a better user experience, stronger security, and the ability to run tasks anywhere, at any time. In this workshop, you’ll get hands-on experience with the new release and learn how to leverage new features like DAG versioning, backfills, data assets, and a new react-based UI. Whether you’re writing traditional ELT/ETL pipelines or complex ML and GenAI workflows, you’ll learn how Airflow 3 will make your day-to-day work smoother and your pipelines even more flexible. This workshop is suitable for intermediate to advanced Airflow users. Beginning users should consider taking the Airflow fundamentals course on the Astronomer Academy before attending this workshop.

Airflow 3 brings several exciting new features that better support MLOps: Native, intuitive backfills Removal of the unique execution date for dag runs Native support for event-driven scheduling These features, combined with the Airflow AI SDK, enable dag authors to easily build scalable, maintainable, and performant LLMOps pipelines. In this talk, we’ll go through a series of workflows that use the Airflow AI SDK to empower Astronomer’s support staff to more quickly resolve problems faced by Astronomer’s customers.

Ensuring high-quality data is essential for building user trust and enabling data teams to work efficiently. In this talk, we’ll explore how the Astronomer data team leverages Airflow to uphold data quality across complex pipelines; minimizing firefighting and maximizing confidence in reported metrics. Maintaining data quality requires a multi-faceted approach: safeguarding the integrity of source data, orchestrating pipelines reliably, writing robust code, and maintaining consistency in outputs. We’ve embedded data quality into the DevEx experience, so it’s always at the forefront instead of in the backlog of tech debt. We’ll share how we’ve operationalized: Implementing data contracts to define and enforce expectations Differentiating between critical (pipeline-blocking) and non-critical (soft) failures Exposing upstream data issues to domain owners Tracking metrics to measure overall data quality of our team Join us to learn practical strategies for building scalable, trustworthy data systems powered by Airflow.

The City of Pittsburgh utilizes Airflow (via Astronomer) for a wide variety of tasks. From employee-focused use cases, like time bank balancing and internal dashboards, to public-facing publication, the City’s data flows through our DAGs from many sources to many sources. Airflow acts as a funnel point and is an essential tool for Pittsburgh’s Data Services team.

As a popular open-source library for analytics engineering, dbt is often combined with Airflow. Orchestrating and executing dbt models as DAGs ensures an additional layer of control over tasks, observability, and provides a reliable, scalable environment to run dbt models. This workshop will cover a step-by-step guide to Cosmos , a popular open-source package from Astronomer that helps you quickly run your dbt Core projects as Airflow DAGs and Task Groups, all with just a few lines of code. We’ll walk through: Running and visualising your dbt transformations Managing dependency conflicts Defining database credentials (profiles) Configuring source and test nodes Using dbt selectors Customising arguments per model Addressing performance challenges Leveraging deferrable operators Visualising dbt docs in the Airflow UI Example of how to deploy to production Troubleshooting We encourage participants to bring their dbt project to follow this step-by-step workshop.