That’s a solid callout, appreciate you pointing it out. I’ll definitely dig into that more.

Was looking for this exact comment. I completely agree with this method, especially if you're testing an entire flow, and not just a UI tool. You want to test the service that interfaces between the API and the dabatase.

I've been writing custom simulation agents (just simple go programs) that simulate different users of my system. I can scale appropriately and see test data flow in. If metabase could generate these simulation agents based on a schema and some instructions, now that would be quite neat! Good job on this first version of the tool, though!

The best synthetic data are those that capture ingestion and action, instead of just relationship.

Relationship is important, but your data structure might capture a virtually infinite number of unexpected behaviors that you would preferably call errors or bugs.

This is well put. I once built a tool called [zefaker] (github.com/creditdatamw/zefaker) to test some data pipelines but never managed to get a good pattern or method for generating data that simulates actions or scenarios that didn't involve too much extra work.

Was hoping this AI dataset generator solves that issue, but i guess it is still early days. Looks good though and using Faker to generate the data locally sounds good as a cost-cutting measure, but also potentially opens room for human-in-the-loop adjustments of the generated data.