Converged Analytics: Copy Once, Query Everywhere

Most attempts to unify analytics still move data to get the job done: faster pipelines, tighter syncs, more automated ETL. That's real progress, but it's still movement — and every copy created has a cost, whether it shows up as stale data, bloated bills, or lost control over where data lives and who can touch it.

In this session, we show a different approach and prove it live on EDB Postgres AI: copy once, query everywhere. Transactional and analytical engines query the same governed data in place, so there's less data moving and fewer pipelines to build and maintain between systems. We'll cover why this shift is happening now, driven by cost, freshness, control, and the need to make AI work on live data, and why moving less data is becoming a bigger lever than moving it faster.

For the demo, we'll load a real dataset onto Apache Iceberg, an open table format, and run it through multiple open engines back to back with no copies in between. Watch EDB Postgres AI hold governed data in place while those engines query it directly, each doing the job it's built for.