Metabase Data Studio

This makes a lot of sense. Without a solid semantic layer, AI is basically guessing. Really like how you’re turning metrics into something reusable and trustworthy. Congrats!

Congrats on the Product Hunt launch! Been enjoying Metabase Data Studio.Noticed that transforms are the recommended path over models now (from this docs). Since transforms already somewhat manage the full table lifecycle (create, refresh, drop); out of curiosity, is index support on output tables in the roadmap?Thanks!

Data cleaning is actually the most challenging part of projects like this. We’re currently building an AI analytics project and are working on data cleaning.

As a non-technical person using Metabase, having verified datasets and predefined metrics that are owned by someone who actually knows what they're doing makes it way easier for me to run the reports i need, and be confident in the answers I get. I haven't asked Metabot yet, but i'm pretty sure she feels the same.

Great to see Metabase still going strong. I used it a couple years ago on a personal project and I liked it a lot. 

The dependency graph feature is what sells this for me. Been burned too many times by renaming a column upstream and only finding out days later when a dashboard breaks. Having that visibility built into the same tool where you define metrics feels right — no more duct-taping dbt + Looker + docs together.

Congrats on the launch! This is a big step forward in making data easier to work with and trust across teams

This is my favorite project that Metabase launched, and I use it every day now. It's a set of tools to run your entire data stack inside Metabase: transforms, definitions, lineage, everything.Here's how I use it every day:- Write SQL (and sometimes Python) transforms and save results straight to the database: like cleaning up messy user signup data and combining it with referral info to make a new table I can query everywhere.- Define metrics once so I don’t have to rethink “what counts as active users” every time: now everyone on the team uses the same definition.- Create clean tables I trust: for example, a revenue table that I know is accurate and ready for dashboards without extra checks.- Trace numbers back when something looks off: like seeing exactly which transform or question a dashboard number came from instead of guessing.- Catch issues early: if a column got renamed and a query breaks, I know immediately which dashboards are affected before anyone asks “why is this number different?”Everything in one place.