One thing I admire about Snowflake is a real commitment to self-cannibalization. They were super out front with Iceberg even though it could disrupt them, because that's what customers were asking for and they're willing to bet they'll figure out how to make money in that new world

Video of their SVP of Product talking about it here: https://youtu.be/PERZMGLhnF8?si=DjS_OgbNeDpvLA04&t=1195

It's not going to scale as well as Snowflake, but it gets you into an Iceberg ecosystem which Snowflake can ingest and process at scale. Analytical data systems are typically trending to heterogenous compute with a shared storage backend -- you have large, autoscaling systems to process the raw data down to something that is usable by a smaller, cheaper query engine supporting UIs/services.

When we first developed pg_lake at Crunchy Data and defined GTM we considered whether it could be a Snowflake competitor, but we quickly realised that did not make sense.

Data platforms like Snowflake are built as a central place to collect your organisation's data, do governance, large scale analytics, AI model training and inference, share data within and across orgs, build and deploy data products, etc. These are not jobs for a Postgres server.

Pg_lake foremost targets Postgres users who currently need complex ETL pipelines to get data in and out of Postgres, and accidental Postgres data warehouses where you ended up overloading your server with slow analytical queries, but you still want to keep using Postgres.

It'll probably be really difficult to set up.

If it's anything like super base, your question the existence of God when trying to get it to work properly.

You pay them to make it work right.