dataspren-analytics/datastudio

dataspren-analytics/datastudio has 79 GitHub stars and 4 forks. GitHot measured 0 stars in 24 hours and 0 in 7 days. Last checked 2026-11-14 07:14 UTC. Last push 2026-03-12.

How these counts are measured

79 stars 4 Forks

What you can do

Notebook interface - Multi-cell notebooks with Python, SQL, and Markdown cells. SQL cells automatically materialize as views accessible from Python.
In-browser execution - Python (Pyodide) and DuckDB run entirely in WebAssembly. Nothing leaves your machine.
Data visualization - Built-in charting (bar, line, area, scatter, pie) powered by ECharts.
File management - Upload, browse, and query CSV, Parquet, JSON, and Excel files.

Measured star growth

in 24h: 0
in 7 days: 0
in 15 days: Collecting history
in 1 month: Collecting history

Net star changes over overlapping periods, not daily totals. Missing measurements are left blank. Stars measure attention, not software quality.

About

A local Python/SQL notebook in the browser for exploring, transforming and visualizing data using WebAssembly.

Data

README.md

Original

DataStudio

Explore and visualize data, entirely in your browser. Powered by WebAssembly and DuckDB. Your data stays on your machine.

Live Demo | Getting Started | Features

DataSpren DataStudio notebook interface with Python, SQL, and charting

Roadmap

External Storage - Mount S3 buckets
AI UDFs - AI SQL UDFs embed(col), cosine_sim(u, v), llm(model, prompt, col)
Ad hoc SQL - .sql file support with executor + viewer

Have a feature request? Open an issue and let us know!

Why DataStudio?

Most data tools require a backend, cloud account, or sending your data somewhere. DataStudio runs entirely in your browser. Python and DuckDB execute via WebAssembly, and files are stored locally using the browser's Origin Private File System. There is no server, no upload, and nothing to install.

This makes it ideal for:

Quick data exploration: drag in a CSV or Parquet file and start querying in seconds
Sensitive data: financial, medical, or proprietary datasets that can't leave your machine
Offline work: everything works without an internet connection after the initial load
Sharing analyses: notebooks use the standard Jupyter .ipynb format

Features

Feature	Description
Notebook interface	Multi-cell notebooks with Python, SQL, and Markdown cells. SQL cells automatically materialize as views accessible from Python.
In-browser execution	Python (Pyodide) and DuckDB run entirely in WebAssembly. Nothing leaves your machine.
Data visualization	Built-in charting (bar, line, area, scatter, pie) powered by ECharts.
File management	Upload, browse, and query CSV, Parquet, JSON, and Excel files.
Data validation	Built-in checks for uniqueness, not-null, accepted values, and custom SQL validations.
Excel editing with SQL	Query and transform Excel files directly using SQL.

Demo

Notebook: Python, SQL, and Charts

Excel Editing

Getting Started

Prerequisites

Node.js 22+
pnpm

Run locally

pnpm install
pnpm dev

Open http://localhost:3000.

Run with Docker

cp .env.example .env
docker compose up --build

Tech Stack

Technology	Role
Next.js + React 19	UI framework
Pyodide	Python compiled to WebAssembly
DuckDB	In-process analytical SQL engine
ECharts	Data visualization
CodeMirror	Code editor
Tailwind CSS + Radix UI	Styling and components

Contributing

Contributions are welcome! Feel free to open an issue or submit a pull request.

License

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