Red Hat’s unified data and AI platform relies on Apache Airflow for orchestration, alongside Snowflake, Fivetran, and Atlan. The platform prioritizes building a dependable data foundation, recognizing that effective AI depends on quality data. Airflow was selected for its predictability, extensive connectivity, reliability, and scalability. The platform now supports business analytics, transitioning from ETL to ELT processes. This has resulted in a remarkable improvement in how we make data available for business decisions. The platform’s capabilities are being extended to power Digital Workers (AI agents) using large language models, encompassing model training, fine-tuning, and inference. Two Digital Workers are currently deployed, with more in development. This presentation will detail the rationale and background of this evolution, followed by an explanation of the architectural decisions made and the challenges encountered and resolved throughout the process of transforming into an AI-enabled data platform to power Red Hat’s business.

Discover how Apache Airflow powers scalable ELT pipelines, enabling seamless data ingestion, transformation, and machine learning-driven insights. This session will walk through: Automating Data Ingestion: Using Airflow to orchestrate raw data ingestion from third-party sources into your data lake (S3, GCP), ensuring a steady pipeline of high-quality training and prediction data. Optimizing Transformations with Serverless Computing: Offloading intensive transformations to serverless functions (GCP Cloud Run, AWS Lambda) and machine learning models (BigQuery ML, Sagemaker), integrating their outputs seamlessly into Airflow workflows. Real-World Impact: A case study on how INTRVL leveraged Airflow, BigQuery ML, and Cloud Run to analyze early voting data in near real-time, generating actionable insights on voter behavior across swing states. This talk not only provides a deep dive into the Political Tech space but also serves as a reference architecture for building robust, repeatable ELT pipelines. Attendees will gain insights into modern serverless technologies from AWS and GCP that enhance Airflow’s capabilities, helping data engineers design scalable, cloud-agnostic workflows.

As data platforms grow in complexity, so do the orchestration needs behind them. Time-based (cron) scheduling has long been the default in Airflow, but dataset-based scheduling promises a more data-aware, efficient alternative. In this session, I’ll share lessons learned from operating Airflow at scale—supporting thousands of DAGs across teams with varied use cases, from simple ETL to complex ML workflows. We’ll explore when dataset scheduling makes sense, the challenges it introduces, and how to evolve your DAG design and platform architecture to make the most of it. Whether you’re migrating legacy workflows or designing new ones, this talk will help you evaluate the right scheduling model for your needs.

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.

At LinkedIn, our data pipelines process exabytes of data, with our offline infrastructure executing 300K ETL workflows daily and 10K concurrent executions. Historically, these workloads ran on our legacy system, Azkaban, which faced UX, scalability, and operational challenges. To modernize our infra, we built a managed Airflow service, leveraging its enhanced developer & operator experience, rich feature set, and strong OSS community support. That initiated LinkedIn’s largest-ever infrastructure migration—transitioning thousands of legacy workflows to Airflow. In this talk, we will share key lessons from migrating massive-scale pipelines with minimal production disruption. We will discuss: Overall Migration Strategy Custom Tooling Enhancements on testing, deployment, and observability Architectural Innovations decoupling orchestration and compute GenAI-powered Migration automating code rewrites Post-Migration Challenges & Airflow 3.0. Attendees will walk away with battle-tested strategies for large-scale Airflow adoption and practical insights into scaling Airflow in enterprise environments.

In the age of Generative AI, knowledge bases are the backbone of intelligent systems, enabling them to deliver accurate and context-aware responses. But how do you ensure that these knowledge bases remain up-to-date and relevant in a rapidly changing world? Enter Apache Airflow, a robust orchestration tool that streamlines the automation of data workflows. This talk will explore how Airflow can be leveraged to manage and update AI knowledge bases across multiple data sources. We’ll dive into the architecture, demonstrate how Airflow enables efficient data extraction, transformation, and loading (ETL), and share insights on tackling challenges like data consistency, scheduling, and scalability. Whether you’re building your own AI-driven systems or looking to optimize existing workflows, this session will provide practical takeaways to make the most of Apache Airflow in orchestrating intelligent solutions.

The journey from ML model development to production deployment and monitoring is often complex and fragmented. How can teams overcome the chaos of disparate tools and processes? This session dives into how Apache Airflow serves as a unifying force in MLOps. We’ll begin with a look at the broader MLOps trends observed by Google within the Airflow community, highlighting how Airflow is evolving to meet these challenges and showcasing diverse MLOps use cases – both current and future. Then, Priceline will present a deep-dive case study on their MLOps transformation. Learn how they leveraged Cloud Composer, Google Cloud’s managed Apache Airflow service, to orchestrate their entire ML pipeline end-to-end: ETL, data preprocessing, model building & training, Dockerization, Google Artifact Registry integration, deployment, model serving, and evaluation. Discover how using Cloud Composer on GCP enabled them to build a scalable, reliable, adaptable, and maintainable MLOps practice, moving decisively from chaos to coordination. Cloud Composer (Airflow) has served as a major backbone in transforming the whole ML experience in Priceline. Join us to learn how to harness Airflow, particularly within a managed environment like Cloud Composer, for robust MLOps workflows, drawing lessons from both industry trends and a concrete, successful implementation.