For the past five years we've focused on production ready Postgres, which is unique from most other database providers. Nothing about that focus changes today, me and the entire team are as laser focused as every on that, but now with an expanded mission of a unified data platform.

Increasingly a first class Postgres experience isn't complete without a seamless analytics experience.

The design of everything from reception to HR to engineering mattered, appreciating and prioritizing design at that level definitely set it apart at a time where that wasn't common.

👋 if you want to come over to near Berkeley

Respect.

If I had known we didn't have to fully manage the Iceberg for folks we might have shipped so much sooner 😂

Was sort of discussed in an office hours session yesterday, you can "sort of" do it with pyIceberg, but only sort of, it's not really an "easy" button for it. But still don't think that's the primary reason people aren't.

My guess would be people are just using Iceberg connectors, see things seem to work, and compaction is an after-thought. That's why IMO good Iceberg support is not a connector feature but an engine feature. Like what Crunchy is doing with their DWH, or, for Kafka, Confluent with Tableflow.

Nope, didn't really get into it in that much detail lots of quick hallway conversations.

With yesterdays launch of logical replication for our Data Warehouse. Now you can still use CDC for your other data pipelines, but for your primary Postgres from operational -> analytical that is solved for you–without buying yet another tool.