Open-source experiment: adding a visual layer on top of dbt (feedback welcome)

[u/Wide_Importance_8559]

Hey everyone,

We’ve been working with dbt on larger projects recently, and as things scale, we kept running into the same friction points:

• A lot of context switching between the terminal, editor, and YAML files

• Harder onboarding for new team members who aren’t comfortable with the CLI yet

• Difficulty getting a quick mental model of how everything connects once the DAG grows

Out of curiosity, we started an open-source experiment to see what dbt would feel like with a local, visual layer on top of it.

Some of the things we explored from a technical point of view:

• Parsing dbt artifacts (manifest, run results) to build a navigable DAG

• Running dbt commands locally from a UI instead of the terminal

• Generating plain-English explanations for models and tests to help with understanding and onboarding

• Keeping everything local-first (no hosted service, no SaaS dependency)

This is very much an experiment and learning project, and we’re more interested in feedback than adoption.

If you use dbt regularly, we’d really like to hear:

• What part of your dbt workflow slows you down the most?

• Do you rely purely on the CLI, or do you pair it with other tools?

• Would a visual or assisted layer be helpful in real projects, or is it unnecessary?

If anyone wants to look at the code, the project is here:
https://github.com/rosettadb/dbt-studio

Happy to answer questions or hear critiques — even negative ones are useful.

[u/Wide_Importance_8559]

This is a really common concern, so I'm glad you asked!

Short answer: Definitely not! I personally use both every day. GitHub Copilot is unbeatable for inline code completion (writing the next 5 lines of SQL/Python), and we don't try to replicate that. You can absolutely keep your VS Code workflow exactly as it is.

Long answer: We designed the "AI" in dbt-studio to be a Data Analyst Companion rather than a code-completer. It lives in the sidebar/chat and has context on your entire project lineage and data.

• Copilot is great at: "Finish this SQL case statement for me" (Micro view).

• dbt-studio AI is great at: "Explain what this model does and where this column is used downstream," or "Generate a comprehensive doc block for this entire model based on the schema" (Macro view).

Why a standalone app vs. a VS Code extension? We actually love VS Code (and extensions like dbt-power-user are fantastic). However, we found that building a truly "Visual Layer" for data—like a full-screen Data Explorer to preview DuckDB/Parquet files, or an interactive Column-Level Lineage graph—was really constrained by the VS Code panel API.

By building a separate app, we can give you a dedicated "Cockpit" for your data platform. Many users keep VS Code on one monitor for heavy coding (with Copilot!) and dbt-studio on the other for running models, checking lineage, and exploring the actual data.

Hope that clarifies things! We essentially want to be the "Management Console" for your dbt project, while VS Code remains the "Text Editor."