In the blog article[1] that linked to, it says "Unified transactional and analytical workloads: Lakebase integrates seamlessly with the Lakehouse, sharing the same storage layer across OLTP and OLAP. This makes it possible to run real-time analytics, machine learning, and AI-driven optimization directly on transactional data without moving or duplicating it."

Is the "without moving or duplicating" part actually a true statement? If the actual table state is only reconstructed by the pageserver, its not like Spark can just read it from S3.

[1] https://www.databricks.com/blog/what-is-a-lakebase

How does it affect HA postgres? (Replicas, consensus, etc). Especially with extensions like citus.

Thanks for offering. In the graph labeled "Prod customer throughput: (higher is better)" eyeballing it within a week you are seeing ~2k qps peak increase over the previous week.

Operationally, how do you handle landing that large of a perf improvement? If my data store changed that much in a week it could break something.