This is an overview of migrating from Apache Airflow to Lakeflow Jobs for modern data orchestration. It covers key differences, best practices and practical examples of transitioning from traditional Airflow DAGs orchestrating legacy systems to declarative, incremental ETL pipelines with Lakeflow. Attendees will gain actionable tips on how to improve efficiency, scalability and maintainability in their workflows.

Building an open data lakehouse? Start with the right blueprint. This session walks through common reference architectures for interoperable lakehouse deployments across AWS, Google Cloud, Azure and tools like Snowflake, BigQuery and Microsoft Fabric. Learn how to design for cross-platform data access, unify governance with Unity Catalog and ensure your stack is future-ready — no matter where your data lives.

What if you could run Spark jobs without worrying about clusters, versions and upgrades? Did you know Spark has this functionality built-in today? Join us to take a look at this functionality — Spark Connect. Join us to dig into how Spark Connect works — abstracting away Spark clusters away in favor of the DataFrame API and unresolved logical plans. You will learn some of the cool things Spark Connect unlocks, including: Moving you from thinking about clusters to just thinking about jobs Making Spark code more portable and platform agnostic Enabling support for languages such as Go