​Open Lakehouse + AI Meetup - Wednesday, October 7, 5:00 PM – 9:00 PM PDT | Bellevue, WA

​​RSVP HERE 👉 https://bit.ly/openlakehouse10-07

​We're bringing together the open source and data engineering community for an evening focused on the latest in open lakehouse and AI architectures! 🚀 Whether you work on data infrastructure, contribute to open source, or want to dive into the future of AI and interoperable lakehouse systems, you’ll fit right in.

​Don't miss this opportunity to accelerate your data journey and contribute to shaping the future of data and AI! ​🌟

​5:00 - 6:00 PM: Registration / Mingling

​6:00 - 6:05 PM: Welcome Remarks / Housekeeping

​6:10 - 6:40 PM: Session #1 - Modern Analytics at GPU Speed: Accelerating Spark, Delta Lake, Iceberg, and Spark Connect - Felix Cheung, NVIDIA

​6:45 - 7:15 PM: Session #2 - A Unified Future for Delta and Apache Iceberg - Micah Kornfield, Databricks

​7:20 - 8:00 PM: Panel Discussion - Connecting the Open Lakehouse and AI Ecosystem

​Panelists: Luke Kim (Spice AI), Shawn Chang (AWS), Haoyan Geng (Databricks)

​8:00 - 9:00 PM: Networking / Light Bites

​Session Descriptions

​Modern Analytics at GPU Speed: Accelerating Spark, Delta Lake, Iceberg, and Spark Connect - Felix Cheung, NVIDIA

​GPU acceleration enables Apache Spark to process modern analytics workloads significantly faster without changing existing Spark applications. This session introduces GPU-accelerated Spark, presents benchmark results and customer success stories, and highlights where acceleration delivers the greatest value—particularly for join-intensive workloads and the Gold layer of the Medallion architecture.

​Beyond compute acceleration, we'll discuss the importance of optimizing data access by accelerating Delta Lake and Apache Iceberg. We'll conclude with an overview of an end-to-end accelerated Spark architecture featuring Spark Connect, illustrating how GPU acceleration, modern data lakes, and analytics work together to deliver faster time to insight.

​A Unified Future for Delta and Apache Iceberg

​Delta Lake and Apache Iceberg™ have converged on similar ideas: columnar metadata for efficient pruning, manifest trees for scalability, and deletion vectors for fast updates. Yet today, we maintain two separate metadata structures, duplicating work and diverging in capabilities.

The next major evolution of Delta Lake reimagines metadata from the ground up. This talk introduces a unified metadata architecture where Delta Lake commits store content metadata directly in Iceberg v4's adaptive metadata tree. Delta Lake gains efficient tree-structured manifests and Iceberg interoperability while preserving the transactional guarantees that Delta users depend on.

​Event Partners