From Postgres to ClickHouse and Back: Building a Unified OLTP + OLAP Database for AI Workloads

Databases are starting to look a lot less “either OLTP or OLAP” and a lot more “both, at once”. Data volumes are growing faster than most application architectures were designed for, and teams are being pushed to adopt OLAP systems earlier in their lifecycle. At the same time, AI-driven products are creating new workloads that need low-latency transactional behavior and high-throughput analytics together. The result is a clear trend: OLTP and OLAP are converging, and the idea of a unified database stack is becoming practical.

Working on ClickHouse’s managed Postgres effort, I have a front-row seat to this shift. Before ClickHouse, I co-founded PeerDB, where we saw data movement from Postgres to ClickHouse accelerate by orders of magnitude over the last couple of years, and it is still growing. That growth is not just about ETL, it signals what users actually want: transactional simplicity with analytics-grade performance, without stitching together a dozen systems.

In this talk, I will explain the pattern we are seeing, why it is accelerating now, and what it implies for the next generation of database platforms. I will then walk through the approach ClickHouse is taking, including managed Postgres, tighter Postgres and ClickHouse integration, and new primitives like pg_clickhouse and pg_stat_ch. We will also cover the replication story (including new “logical replication v2” style ideas), and the set of levers required to get closer to sub-second freshness and low operational overhead.

Finally, I will zoom out to the bigger picture: a unified database is not just “Postgres + OLAP”. It requires re-architecting parts of the stack so applications do not have to carry the abstraction burden. I will share what “world-class” looks like here, the remaining technical challenges, and a realistic path to making unified OLTP + OLAP the default for fast-growing AI workloads.