Looks great. Quite straightforward. Regarding BKs - has the assistant successfully identified multi-part BKs? I would have expected that something like name+address+postal_code might have been a candidate (especially if there was no customerid). Have you tested that? Also, is there a way to suggest a different BK if you don't agree with the one the assistant suggested?

Regarding BKs - has the assistant successfully identified multi-part BKs? I would have expected that something like name+address+postal_code might have been a candidate (especially if there was no customerid). Have you tested that?

Again, they are talking about technologies not methodologies. People forget DV 2.x is not just a way to model data but architecture and methodology too. All of it still applies in the modern data landscape. Dan's new DV 2.1 course now includes discussions about data lakes and PSAs and virtualization. In the end you still have to a plan and approach and at a minimum you need the business ontology and taxonomy to properly build a virtualization layer that makes sense to the business. You can use DV for all of that! A logical DV model can certainly be used to build the model inside a virtualization tool.

Check Snowflake.com/blog for lake house articles. Also their resources page for white papers and presos. I know some of the Field CTOs and SE have done talks and posts over the last few years. Check LinkedIn postings from Kevin Bair at Snowflake as he works exclusively on data cloud architecture these days.

Sadly too many people do not understand the difference between an architecture (e.g., Medallion) and a methodology like Data Vault 2.x.

Check out https://datameshlearning.com/ . They also have a Slack channel with lots of activity on it.