r/DuckDB

https://preview.redd.it/okxv3i49p90h1.png?width=1536&format=png&auto=webp&s=24587f8e3f6db2eab1bebd7c7efc3642e6eff8da

Current support includes:

• Schema Editor integration
• Visual inspection/editing of nested objects & arrays
• special editors for images, blobs, UUIDs, etc.
• AI-assisted SQL Editor
• Direct editing of VARIANT values in Data Editor

Available on macOS, Windows and Linux.

Curious how DuckDB users currently work with semi-structured data and whether visual tooling around VARIANT is useful in practice.

More details:
https://valentina-db.com/dokuwiki/doku.php?id=valentina:articles:vstudio_v173_duck_variant

Hi everyone!

I'm working on a no-code agent builder that builds conversational analytics agents that respond with interactive charts and UI. I've implemented dedicated support for DuckDB to make it easier to hook up databases for agents to query.

Here's an example agent I've built using the project: https://console.thesys.dev/app/-2PqdNdGjSQb6WrdYI9pR

Feedback would be highly appreciated!

While building a local data warehouse on GitHub Archive data, we ran into a slightly frustrating DuckDB behavior: columns that happen to contain only NULL values in the first file read get inferred as the generic JSON type. When real values show up in later files, staging breaks.

Our fix: a synthetically generated canonical sample — a single JSON file with at least one non-null value for every column. It gets passed to read_json_auto alongside every real archive file, ensuring stable type inference from the start. A WHERE clause filters it out of the actual results.

The project is a fully local data warehouse on GitHub Archive — ~700-attribute variable JSON events, schema discovery in Python that auto-generates dbt models, and a star schema with mart tables that Rill can explore interactively at sub-second speed across 25M+ events.

The canonical sample approach feels like something others must have hit too — curious how you've handled this, or whether there's a cleaner DuckDB-native solution we missed.

Repo: idesis-gmbh/githubexperiments

Four months ago I got tired of every "just look at this CSV" task turning into a 45-minute detour. So I built DuckViz, and it kept growing into something I didn't quite plan.

Here's what happens when you drop a CSV (or Parquet, or JSON) into app.duckviz.com:

Second 1 — it's a dashboard. DuckDB compiled to WebAssembly is doing the querying right there in your browser tab. Your file never uploads. Open devtools, check the network panel, you'll see nothing leave. Charts, filters, drilldowns, all of it — instant.

Second 30 — you ask it a question in plain English. "Show me revenue by region for customers older than 6 months." It generates the chart. The AI doesn't see your data either — it only sees your schema and writes the SQL.

Minute 2 — it's a report. Switch to Report mode and you get a rich-text doc you can write narrative in, with live charts embedded inline. Like Notion, but the charts actually query your data.

Minute 5 — it's a slide deck. Same data, presentation layout. Drop charts on slides, present from the browser. Your quarterly review just wrote itself.

Minute 6 — it looks like your brand. 10 built-in themes plus a full custom theme builder. Pick a vibe or paste your brand colors.

Minute 10 — it's inside your own product. the same thing renders inside your SaaS. Your customers' data never touches your servers either. That's the part that closes deals with security teams.

It's free to try, no signup for the demo, no email gate. The full thing — saved projects, sharing, AI — is behind a free account.

duckviz.com

Checkout the sample dashboards created using DuckViz for mock API's

https://duckviz-examples.vercel.app/