Traditional time-based scheduling in Airflow can lead to inefficiencies and delays. With Airflow 3.0, we can now leverage native event-driven DAG execution, enabling workflows to trigger instantly when data arrives—eliminating polling-based sensors and rigid schedules. This talk explores real-time orchestration using Airflow 3.0 and Google Cloud Pub/Sub. We’ll showcase how to build an event-driven pipeline where DAGs automatically trigger as new data lands, ensuring faster and more efficient processing. Through a live demo, we’ll demonstrate how Airflow listens to Pub/Sub messages and dynamically triggers dbt transformations only when fresh data is available. This approach improves scalability, reduces costs, and enhances orchestration efficiency. Key Takeaways: How event-driven DAGs work vs. traditional scheduling, Best practices for integrating Airflow with Pub/Sub,Eliminating polling-based sensors for efficiency,Live demo: Event-driven pipeline with Airflow 3.0, Pub/Sub & dbt. This session will showcase how Airflow 3.0 enables truly real-time orchestration.

Looking for a way to streamline your data workflows and master the art of orchestration? As we navigate the complexities of modern data engineering, Airflow’s dynamic workflow and complex data pipeline dependencies are starting to become more and more common nowadays. In order to empower data engineers to exploit Airflow as the main orchestrator, Airflow Datasets can be easily integrated in your data journey. This session will showcase the Dynamic Workflow orchestration in Airflow and how to manage multi-DAGs dependencies with Multi-Dataset listening. We’ll take you through a real-time data pipeline with Pub/Sub messaging integration and dbt in Google Cloud environment, to ensure data transformations are triggered only upon new data ingestion, moving away from rigid time-based scheduling or the use of sensors and other legacy ways to trigger a DAG.

This talk will cover in high overview the architecture of a data product DAG, the benefits in a data mesh world and how to implement it easily. Airflow is the de-facto orchestrator we use at Astrafy for all our data engineering projects. Over the years we have developed deep expertise in orchestrating data jobs and recently we have adopted the “data mesh” paradigm of having one Airlfow DAG per data product. Our standard data product DAGs contain the following stages: Data contract: check integrity of data before transforming the data Data transformation: applies dbt transformation via a kubernetes pod operator Data distribution: mainly informing downstream applications that new data is available to be consumed For use cases where different data products need to be finished before triggering another data product, we have a mechanism with an engine in between that keeps track of finished dags and triggers DAGs based on a mapping table containing data products dependencies.