Beyond Analytics

Three years ago, no one imagined data orchestration could power more than core ETL. But the AI era changed everything. Today, orchestration sits at the heart of the modern data stack — enabling AI, ML, and data products behind the tools and experiences we rely on every day.

Join Astronomer Co-Founder Pete DeJoy and CTO Julian LaNeve as they reflect on that evolution and share their vision for the future of DataOps. You’ll get an exclusive first look at new innovations that make Airflow more accessible and powerful than ever, and see how forward-thinking teams are building what’s next.

Ramp is the financial operating system helping businesses save time and save money. Data powers every decision we make and powers many of our core functions, from internal analytics & reporting to ML and AI-driven automation. Underpinning all of this is Ramp's data platform, with Airflow at the heart of it. In this session, Director of Engineering Ryan Delgado will talk about his team's journey from focusing on bread-and-butter ETL to tackling cutting-edge engineering problems in ML and AI - and the hard lessons his team learned along the way.

Right now every organization, whether agile startups or large enterprises, is looking to implement GenAI initiatives, from customer-facing applications to the streamlining of internal processes. Airflow 3 is ready for this challenge: from data preparation to inference execution to orchestrating multi-agent workflows. In this talk, you’ll learn about practical patterns and key Airflow features for GenAI pipelines, along with links to additional resources and example code.

Laurel is the company behind the world’s first AI Time platform, streamlining workforce management with AI-driven timekeeping solutions. The company recently raised a $100M Series C based on increasing demand for their offerings. In this session, Senior ML Engineer Jacob Dodd shares how the team uses Airflow for context engineering, including orchestrating Retrieval-Augmented Generation (RAG) pipelines to automate prompt creation for large language models. Discover how they deliver production-grade context through a mix of real-time systems and scheduled AI workflows.

We have just begun to grasp the potential for AI both in our personal lives and at work. But a significant, current blocker keeping us from unlocking the full value of AI is the lack of a context layer. What if switching between models and providers didn’t mean having to start from scratch reminding AI tools who you are, what your preferences and tastes are, the way you like to receive outputs, etc.?

Data orchestration is not just plumbing; it is a strategic capability that determines how quickly and effectively your organization turns data into value. Large-scale data initiatives often fail when orchestration is treated as an afterthought or driven solely by technical goals, without a clear connection to business priorities. In this session, Airbrx CEO Ben Tallman and CTO Michael Bissell share how an independent orchestration layer built on Apache Airflow, decoupled from storage and compute, puts your team in control of its roadmap, eliminates vendor lock-in, and supports AI-driven products, smooth migrations, and cross-platform workflows. By elevating orchestration in this way, you can ensure your DataOps practice is tightly aligned with the outcomes your business needs most.