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.

In today’s data-driven world, effective workflow management and AI are crucial for success. However, there’s a notable gap between Airflow and AI. Our presentation offers a solution to close this gap. Proposing MCP (Model Context Protocol) server to act as a bridge. We’ll dive into two paths: AI-Augmented Airflow: Enhancing Airflow with AI to improve error handling, automate DAG generation, proactively detect issues, and optimize resource use. Airflow-Powered AI: Utilizing Airflow’s reliability to empower LLMs in executing complex tasks, orchestrating AI agents, and supporting decision-making with real-time data. Key takeaways: Understanding how to integrate AI insights directly into your workflow orchestration. Learning how MCP empowers AI with robust orchestration capabilities, offering full logging, monitoring, and auditability. Gaining insights into how to transform LLMS from a reactive responder to a proactive, intelligent, and reliable executor. Inviting you to explore how MCP can help workflow management, making AI-driven decisions more reliable and turning workflow systems into intelligent, autonomous agents.

Our development workflows look dramatically different than they did a year ago. Code generation, automated testing, and AI-assisted documentation tools are now part of many developers’ daily work. Yet as these tools reshape how we code, I’ve noticed something worth examining: while our toolbox is changing rapidly, the core of being a good developer hasn’t. Problem-solving, collaborative debugging, and systems thinking remain as crucial as ever. In this keynote, I’ll share observations about: Which parts of our workflow are genuinely enhanced by new tools. The development skills that continue to separate good code from great code. How teams can collaborate effectively when everyone’s tools are evolving. What Airflow’s journey teaches us about balancing innovation with stability. No hype or grand pronouncements—just an honest look at incorporating new tools while preserving the craft that makes us developers in the first place.

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.

As organizations increasingly rely on data-driven applications, managing the diverse tools, data, and teams involved can create challenges. Amazon SageMaker Unified Studio addresses this by providing an integrated, governed platform to orchestrate end-to-end data and AI/ML workflows. In this workshop, we’ll explore how to leverage Amazon SageMaker Unified Studio to build and deploy scalable Apache Airflow workflows that span the data and AI/ML lifecycle. We’ll walk through real-world examples showcasing how this AWS service brings together familiar Airflow capabilities with SageMaker’s data processing, model training, and inference features - all within a unified, collaborative workspace. Key topics covered: Authoring and scheduling Airflow DAGs in SageMaker Unified Studio Understanding how Apache Airflow powers workflow orchestration under the hood Leveraging SageMaker capabilities like Notebooks, Data Wrangler, and Models Implementing centralized governance and workflow monitoring Enhancing productivity through unified development environments Join us to transform your ML workflow experience from complex and fragmented to streamlined and efficient.

As data workloads grow in complexity, teams need seamless orchestration to manage pipelines across batch, streaming, and AI/ML workflows. Apache Airflow provides a flexible and open-source way to orchestrate Databricks’ entire platform, from SQL analytics with Materialized Views (MVs) and Streaming Tables (STs) to AI/ML model training and deployment. In this session, we’ll showcase how Airflow can automate and optimize Databricks workflows, reducing costs and improving performance for large-scale data processing. We’ll highlight how MVs and STs eliminate manual incremental logic, enable real-time ingestion, and enhance query performance—all while maintaining governance and flexibility. Additionally, we’ll demonstrate how Airflow simplifies ML model lifecycle management by integrating Databricks’ AI/ML capabilities into end-to-end data pipelines. Whether you’re a dbt user seeking better performance, a data engineer managing streaming pipelines, or an ML practitioner scaling AI workloads, this session will provide actionable insights on using Airflow and Databricks together to build efficient, cost-effective, and future-proof data platforms.

As data workloads grow in complexity, teams need seamless orchestration to manage pipelines across batch, streaming, and AI/ML workflows. Apache Airflow provides a flexible and open-source way to orchestrate Databricks’ entire platform, from SQL analytics with Materialized Views (MVs) and Streaming Tables (STs) to AI/ML model training and deployment. In this session, we’ll showcase how Airflow can automate and optimize Databricks workflows, reducing costs and improving performance for large-scale data processing. We’ll highlight how MVs and STs eliminate manual incremental logic, enable real-time ingestion, and enhance query performance—all while maintaining governance and flexibility. Additionally, we’ll demonstrate how Airflow simplifies ML model lifecycle management by integrating Databricks’ AI/ML capabilities into end-to-end data pipelines. Whether you’re a dbt user seeking better performance, a data engineer managing streaming pipelines, or an ML practitioner scaling AI workloads, this session will provide actionable insights on using Airflow and Databricks together to build efficient, cost-effective, and future-proof data platforms.

This talk explores EDB’s journey from siloed reporting to a unified data platform, powered by Airflow. We’ll delve into the architectural evolution, showcasing how Airflow orchestrates a diverse range of use cases, from Analytics Engineering to complex MLOps pipelines. Learn how EDB leverages Airflow and Cosmos to integrate dbt for robust data transformations, ensuring data quality and consistency. We’ll provide a detailed case study of our MLOps implementation, demonstrating how Airflow manages training, inference, and model monitoring pipelines for Azure Machine Learning models. Discover the design considerations driven by our internal data governance framework and gain insights into our future plans for AIOps integration with Airflow.

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.

Airflow powers thousands of data and ML pipelines—but in the enterprise, these pipelines often need to interact with business-critical systems like ERPs, CRMs, and core banking platforms. In this demo-driven session we will connect Airflow with Control-M from BMC and showcase how Airflow can participate in end-to-end workflows that span not just data platforms but also transactional business applications. Session highlights Trigger Airflow DAGs based on business events (e.g., invoice approvals, trade settlements) Feed Airflow pipeline outputs into ERP systems (e.g., SAP) or CRMs (e.g., Salesforce) Orchestrate multi-platform workflows from cloud to mainframe with SLA enforcement, dependency management, and centralized control. Provide unified monitoring and auditing across data and application layers

Airflow powers thousands of data and ML pipelines—but in the enterprise, these pipelines often need to interact with business-critical systems like ERPs, CRMs, and core banking platforms. In this demo-driven session we will connect Airflow with Control-M from BMC and showcase how Airflow can participate in end-to-end workflows that span not just data platforms but also transactional business applications. Session highlights Trigger Airflow DAGs based on business events (e.g., invoice approvals, trade settlements) Feed Airflow pipeline outputs into ERP systems (e.g., SAP) or CRMs (e.g., Salesforce) Orchestrate multi-platform workflows from cloud to mainframe with SLA enforcement, dependency management, and centralized control. Provide unified monitoring and auditing across data and application layers

In today’s data-driven world, scalable ML infrastructure is mission-critical. As ML workloads grow, orchestration tools like Apache Airflow become essential for managing pipelines, training, deployment, and observability. In this talk, I’ll share lessons from building distributed ML systems across cloud platforms, including GPU-based training and AI-powered healthcare. We’ll cover patterns for scaling Airflow DAGs, integrating telemetry and auto-healing, and aligning cross-functional teams. Whether you’re launching your first pipeline or managing ML at scale, you’ll gain practical strategies to make Airflow the backbone of your ML infrastructure.

As the demand for data products grows, data engineering teams face mounting pressure to deliver more and even faster, often becoming bottlenecks. Astro IDE changes the game. Astro IDE is an AI-powered code editor built for Apache Airflow. It helps data teams go from idea to production in minutes—generating production-ready DAGs, enabling in-browser testing, and integrating directly with Git. In this session, see how Astro IDE accelerates DAG creation, debugging, and deployment so data engineering teams can deliver more, 10x faster.

As the demand for data products grows, data engineering teams face mounting pressure to deliver more and even faster, often becoming bottlenecks. Astro IDE changes the game. Astro IDE is an AI-powered code editor built for Apache Airflow. It helps data teams go from idea to production in minutes—generating production-ready DAGs, enabling in-browser testing, and integrating directly with Git. In this session, see how Astro IDE accelerates DAG creation, debugging, and deployment so data engineering teams can deliver more, 10x faster.

Vayu is a conversational copilot for Apache Airflow, developed at Prevalent AI to help data engineers manage, troubleshoot, and fix pipelines using natural language. Deployments often fail silently due to misconfigurations, missing connections, or runtime issues impossible to identify in unit tests. Vayu tackles these via a troubleshooting agent that inspects logs, metrics, configs, and runtime state to find root causes and suggest fixes saving engineers significant troubleshooting time. It can also apply approved fixes to DAG code and commit them to your version control system. Key Capabilities: Troubleshooting Agent: Inspects logs, configs, variables, and connections to find root causes and suggest fixes. Pipeline Mechanic Agent: Suggests code-level fixes e.g., missing connections or bad imports and, once approved, commits them to version control. DAG Manager Agent: Understands DAG logic, suggests improvements, and can trigger DAGs conversationally. Architecture: Built with open-source tools including Google ADK as the orchestration layer and a custom Airflow MCP server based on the FastMCP framework. LLMs never access Airflow directly. The full codebase will be open-sourced.

On March 13th, 2025, Amazon Web Services announced General Availability of Amazon SageMaker Unified Studio, bringing together AWS machine learning and analytics capabilities. At the heart of this next generation of Amazon SageMaker sits Apache Airflow. All SageMaker Unified Studio users have a personal, open-source Airflow deployment, running alongside their Jupyter notebook, enabling those users to easily develop Airflow DAGs that have unified access to all of their data. In this talk, I will go into details around the motivations for choosing Airflow for this capability, the challenges with incorporating Airflow into such a large and diverse experience, the key role that open-source plays, how we’re leveraging GenAI to make that open source development experience better, and the goals for the future of Airflow in SageMaker Unified Studio. Attendees will leave with a better understanding of the considerations they need to make when choosing Airflow as a component of their enterprise project, and a greater appreciation of how Airflow can power advanced capabilities.