@Web Do some research on https://somecompany.com and write up a detailed overview of what the company does. What might their database schema look like?
I need you to build a mock database for them in duckdb for a demo
Create a uv project and write a python script to add demo data. Use Faker.
@Web research how many customers they have. Make the database to appropriate scale.
It uses GPT-4o to generate a detailed schema and business rules based on a few dropdowns (like business type, schema structure, and row count). Then Faker fills in the rows using those rules, which keeps it fast and cheap.
You can preview the data, export as CSV or SQL, or spin up Metabase with one click to explore the data. It’s open-source, still in early stages, but wanted to share, get feedback and see how you'd improve it.
Cool to see Metabase is enabling contributions to the ecosystem this way! :)
https://claude.ai/public/artifacts/eb7d8256-6d21-4c85-af9b-c...
I used this GitHub repo as context and Claude Opus 4 to create this artifact
It was pretty “dumb,” and used thispersondoesnotexist.com for profile pics.
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!
Relationship is important, but your data structure might capture a virtually infinite number of unexpected behaviors that you would preferably call errors or bugs.
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.