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 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.

Airflow, an open-source platform for orchestrating complex data workflows, is widely adopted for its flexibility and scalability. However, as workflows grow in complexity and scale, optimizing Airflow performance becomes crucial for efficient execution and resource utilization. This session delves into the importance of optimizing Airflow performance and provides strategies, techniques, and best practices to enhance workflow execution speed, reduce resource consumption, and improve system efficiency. Attendees will gain insights into identifying performance bottlenecks, fine-tuning workflow configurations, leveraging advanced features, and implementing optimization strategies to maximize pipeline throughput. Whether you’re a seasoned Airflow user or just getting started, this session equips you with the knowledge and tools needed to optimize your Airflow deployments for optimal performance and scalability. We’ll also explore topics such as DAG writing best practices, monitoring and updating Airflow configurations, and database performance optimization, covering unused indexes, missing indexes, and minimizing table and index bloat.