Chat, Query, and Transform Multi-GB Files - In Natural Language, Right in Your Browser with DuckDB.
- Amar Harolikar
- 6 days ago
- 2 min read
Updated: 5 days ago
Analyzed a 1.6 GB, 11-million-row CSV file on a custom build of SQL Rooms + DuckDB. The entire analysis- querying and charts ran 100% in the local browser. No backend. No remote DB. And fast.
Built on the open-source SQL Rooms (sqlrooms.org) framework from Foursquare, customized for my analytics workflows
📌 What you can do
▸ Process hundreds of MBs, millions of rows - fully local
▸ Merge, append, transform, analyze, and visualize with AI
▸ Chat with your data in natural language
▸ No remote database or upload
▸ File types: CSV, TSV, pipe-delimited, Parquet
▸ Export tables
📌 Live App at
Part of the broader suite of Database AI tools - 10 open source micro-apps for querying, transforming, and analyzing databases in natural language. From remote databases to voice AI.
📌 To Try it:
1. Get a free Gemini API key from aistudio.google.com. (or use OpenAI / Claude). Keys stay fully local in your browser.
2. Explore with the default Tour de France dataset & starter prompts
3. Or try Cricket Data (100 MB- 1.5 GB) on my drive
4. Upload your own files, run queries and visualize directly in browser
📌 Is it 100% Local? What goes out?
▸ File data remains in the browser's DuckDB instance.
▸ LLM receives schema, sample rows, and your query results.
▸ API calls go directly from browser → LLM (except OpenAI, routed via proxy for CORS).
📌 Can it be 100% local?
Yes - the original SQL Rooms repo supports Ollama for fully offline use.
📌 Memory & Limits
Handles multiple files of a few hundred MBs each with ease. For larger volumes, performance depends on your device memory. DuckDB-WASM defaults can be tuned if your hardware supports it.
📌 Code & Docs
🔗 GitHub (custom version): https://github.com/amararun/sql-rooms-tigzig-final
🔗 Docs: on app site → Hit “Docs” on top
📌 Original Project (Credit)
▸ SQL Rooms AI - the one I customized: sqlrooms-ai.netlify.app
▸ SQL Rooms Example Apps & GitHub: sqlrooms.org/examples.html
▸ Release Post from Vikram Gundeti: https://lnkd.in/gfFyD-gK
▸ Architected by Ilya Boyandin
📌For those testing or customizing:
▸ App Architecture file for AI Coders (humans welcome): Included in the GitHub repo - explains architecture, modifications, and deployment details for quick reuse.
🔗 App link – https://app.tigzig.com/sql-rooms
🔗 GitHub repo link – https://github.com/amararun/sql-rooms-tigzig-final
▸ Sample Datasets:
1. Consolidated Cricket Data (2003–2025, Test + ODI + T20 + Others ): 1.6 GB, 11 million rows.
2. Individual match files: 100–300 MB each, for testing multi-file merges.
🔗Drive link – https://drive.google.com/drive/folders/1VHD9UzeYaJF_dBPecnjucHpoGocf4IvR?usp=drive_link
▸ Example Prompt:
- This is ball-by-ball data i.e. one row = one ball. For ODI and T20, find Top 10 strikers by total runs off the bat, with total number of matches, total number of overs, and run rates per match/over, and share in table format. Separate tables for T20 and ODI.
- Share two charts, one for each match type – dual axis – with run rate per over and run rate per match – one as a bar and the other as a line.
