February 22, 2026
No cloud, loud crowd
Show HN: Data Studio – Open-Source Data Notebooks
A private, in-browser data lab has geeks cheering—and warning your RAM
TLDR: DataStudio is a browser-based, open-source data notebook that keeps files on your machine, winning big privacy points. The community is split between hype for local, offline workflows and worries about RAM limits and AI add-ons, making it a lightning rod for “simple tool vs. feature creep” debates.
DataStudio just dropped a “no cloud, no install” data notebook that runs entirely in your browser using WebAssembly (code that runs fast in the browser) and DuckDB (a zippy spreadsheet-on-steroids database). The crowd went wild for the promise of privacy—your files never leave your machine—and the ability to poke at CSVs and Excel sheets offline. One fan called it “the dream: drag a file, query, chart, done,” while others loved that it saves standard Jupyter notebooks for easy sharing.
But not everyone’s lighting celebratory LEDs. Skeptics rushed in with “cool demo, but what happens when my laptop meets a 10GB file?” The RAM panic was real, and a few warned that browser storage can be fragile if you clear data. The roadmap’s AI functions (yes, calling a chatbot from SQL) sparked memes and side-eyes in equal measure: half the thread yelled “bloat,” the other half yelled “future.” Meanwhile, spreadsheet warriors rejoiced at “Excel editing with SQL,” predicting the office politics it will unleash.
Comparisons flew: JupyterLite, Observable, and every DuckDB toy under the sun. Still, the MIT license and offline-first angle had privacy folks grinning. TL;DR: it’s a slick, local-first notebook with charts, Python, and SQL—WebAssembly magic, DuckDB speed—and a comment section split between cheers, fears, and memes about screaming fans named RAM.
Key Points
- •DataStudio is an open-source, browser-based data notebook that runs Python and DuckDB via WebAssembly with local storage using the Origin Private File System.
- •It supports Python, SQL, and Markdown cells, automatic SQL view materialization, and built-in charts powered by ECharts.
- •File management includes uploading and querying CSV, Parquet, JSON, and Excel, with data validation checks and SQL-based Excel editing.
- •Roadmap items include S3 bucket mounting, AI SQL UDFs (embed, cosine_sim, llm), and ad hoc SQL support for .sql files with an executor and viewer.
- •Setup requires Node.js 22+ and pnpm for local development, or Docker; the project is MIT-licensed © 2026 by Dataspren.