Bringing the Operational and Analytical Worlds Together with Lakebase

As database workloads increasingly move into large shared-nothing cloud datacenters, the bits storing operational data, analytical tables, streams, etc all sit together on the same disks in the cloud. This creates new opportunities to unify the capabilities of operational and analytical systems, while being mindful of "one size fits all" pitfalls. I'll discuss how Databricks and Neon are exploring this opportunity with Lakebase, an architecture for OLTP DBMSes that leverages open formats and cloud object stores to also enable efficient analytics on the same data and easy interop between the two worlds. Furthermore, since it wouldn't be a 2025 keynote without AI, I'll explain how we are seeing agents change the demand on both types of systems and appear to be resulting in more "analytics-like" workloads on OLTP databases and more "OLTP-like" workloads on analytical ones, primarily by issuing much larger numbers of small exploratory queries. These trends create many exciting new challenges for the research community.