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simonw
datasette
Python2017–2026
simonw / datasette

datasette

An open source multi-tool for exploring and publishing data

36Distinct people
77Mentions
1Explicit recommenders
11.6KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

An open source multi-tool for exploring and publishing data

GitHub

Where it is used

No documented product use was verified in the last source check.

What people are saying

My favorite incarnation of this idea is probably Datasette. https://github.com/simonw/datasette

rpdillon used a recommendation phrase. One indexed comment matches. This is a phrase match, not a verdict.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

Mentioned in the same Hacker News threads. That is not proof they are substitutes.

This week on Hacker News

Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 12:03:30 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year77 dated mentions

Observed mentions: 2017-11-14 07:56:02 UTC to 2026-08-05 02:35:12 UTC.

051015202017-01-01 00:00:00 UTC to 2018-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments20172018-01-01 00:00:00 UTC to 2019-01-01 00:00:00 UTC (exclusive): 6 mentions; 0 submissions, 6 comments2019-01-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 5 mentions; 1 submissions, 4 comments2020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 21 mentions; 1 submissions, 20 comments2021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 11 mentions; 2 submissions, 9 comments20212022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 12 mentions; 0 submissions, 12 comments2023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 5 mentions; 0 submissions, 5 comments2024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 5 mentions; 0 submissions, 5 comments2025-01-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 3 mentions; 0 submissions, 3 comments2026-01-01 00:00:00 UTC to 2027-01-01 00:00:00 UTC (exclusive): 8 mentions; 1 submissions, 7 comments. Incomplete calendar period.2026*

* An incomplete calendar period is marked with an outlined bar. Its count is not directly comparable with a full period.

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20172017-01-01 00:00:00 UTC2018-01-01 00:00:00 UTC101
20182018-01-01 00:00:00 UTC2019-01-01 00:00:00 UTC606
20192019-01-01 00:00:00 UTC2020-01-01 00:00:00 UTC514
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC21120
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC1129
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC12012
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC505
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC505
20252025-01-01 00:00:00 UTC2026-01-01 00:00:00 UTC303
2026 (partial)2026-01-01 00:00:00 UTC2027-01-01 00:00:00 UTC817

From the project README

An excerpt from the repository, fetched Nov 13, 2026.

Read the project’s introduction
An open source multi-tool for exploring and publishing data Datasette is a tool for exploring and publishing data. It helps people take data of any shape or size and publish that as an interactive, explorable website and accompanying API. Datasette is aimed at data journalists, museum curators, archivists, local governments, scientists, researchers and anyone else who has data that they wish to share with the world. Explore a demo, watch a video about the project or try it out on GitHub Codespaces. datasette.io is the official project website Latest Datasette News Comprehensive documentation: https://docs.datasette.io/ Examples: https://datasette.io/examples Live demo of current main branch: https://latest.datasette.io/ Questions, feedback or want to talk about the project? Join our Discord Want to stay up-to-date with the project? Subscribe to the Datasette newsletter for tips, tricks and news on what's new in the Datasette ecosystem. Installation If you are on a Mac, Homebrew is the easiest way to install Datasette: brew install datasette You can also install it using pip or pipx: pip install datasette Datasette requires Python 3.11 or higher. We also have detailed installation instructions covering other options such as Docker. Basic usage datasette serve path/to/database.db This will start a web server on port 8001 - visit http://localhost:8001/ to access the web interface. serve is the default subcommand, you can omit it if you like. Use Chrome on OS X? You can run datasette against your browser history like so: datasette ~/Library/Application\ Support/Google/Chrome/Default/History --nolock Now visiting http://localhost:8001/History/downloads will show you a web interface to browse your downloads data: metadata.json If you want to include licensing and source information in the generated datasette website you can do so using a JSON file that looks something like this: { "title": "Five Thirty Eight", "license": "CC Attribution 4.0 License", "license_url": "http://creativecommons.org/licenses/by/4.0/", "source": "fivethirtyeight/data on GitHub", "source_url": "https://github.com/fivethirtyeight/data" } Save this in metadata.json and run Datasette like so: datasette serve fivethirtyeight.db -m metadata.json The license and source information will be displayed on the index page and in the footer. They will also be included in the JSON produced by the API. datasette publish If you have Heroku or Google Cloud Run configured, Datasette can deploy one or more SQLite databases to the internet with a single command: datasette publish heroku database.db Or: datasette publish cloudrun database.db This will create a docker image containing both the datasette application and the specified SQLite database files. It will then deploy that image to Heroku or Cloud Run and give you a URL to access the resulting website and API. See Publishing data in the documentation for more details. Datasette Lite Datasette Lite is Datasette packaged using WebAssembly so that it runs entirely in your browser, no Python web application server required. Read more about that in the Datasette Lite documentation.
Read the full README on GitHub

What Hacker News says

5 sources
Here's a way to scrape URLs to JSON/YAML and then build static HTML with Hugo in a GitHub Action: https://github.com/jackyzha0/hugo-obsidian datasette is a webapp and CLI built on SQLite and Python. datasette-lite is the pyodide + WebAssembly build of datasette which can be served as static HTML, JS, and WASM SQlite. datasette: https://github.com/simonw/datasette : > Datasette is a tool for exploring and publishing data. It helps people take data of any shape or size and publish that as an interactive, explorable website and accompanying API. > Datasette is aimed at data journalists, museum curators, archivists, local governments, scientists, researchers and anyone else who has data that they wish to share with the world. From "Deploying a live Datasette demo when the tests pass" (2022) https://til.simonwillison.net/github-actions/deploy-
Read the rest
live-dem... : datasette publish vercel fixtures.db [...] The `datasette publish` command supports Google Cloud Run, Heroku, Vercel, Fly, [Full Container or Serverless] https://docs.datasette.io/en/stable/publish.html datasette-lite: https://github.com/simonw/datasette-lite : > You can use this tool to open any SQLite database file that is hosted online and served with a `access-control-allow-origin: ` CORS header. Files served by GitHub Pages automatically include this header, as do database files that have been published online using `datasette publish`.* > [...] You can paste in the "raw" URL to a file, but Datasette Lite also has a shortcut: if you paste in the URL to a page on GitHub or a Gist it will automatically convert it to the "raw" URL for you > To load a Parquet file, pass a URL to `?parquet=` > [...] https://lite.datasette.io/?parquet=https://github.com/Terada...* There are various *-to-sqlite utilities that load data into a SQLite database for use with e.g. datasette. E.g. Pandas with `dtype_backend='arrow'` saves to Parquet. datasette plugins are written in Python and/or JS w/ pluggy: https://docs.datasette.io/en/stable/plugins.html https://datasette.io/plugins datasette-scraper scrapes sitemaps.xml and crawls though it could surely be repurposed to instead scrape a list of code.json URLs within the datasette process, which is powered by asyncio and the asynchronous uvicorn ASGI HTTP web server. datasette-scraper/#architecture: https://github.com/cldellow/datasette-scraper/#architecture (TIL datasette-scraper parses HTML with selectolax; and Selectolax with Modest or Lexbor is ~25x faster at HTML parsing than BeautifulSoup in the selectolax benchmark: https://github.com/rushter/selectolax#simple-benchmark ) (Apache Nutch is a Java-based web crawler which supports e.g. CommonCrawl (which backs various foundational LLMs)) https://en.wikipedia.org/wiki/Apache_Nutch#Search_engines_bu... . But extruct extracts more types of metadata and data than Nutch AFAIU: https://github.com/scrapinghub/extruct ) datasette-graphql adds a GraphQL HTTP API to a SQLite database: https://datasette.io/plugins/datasette-graphql plugins?q=sqlite: https://datasette.io/plugins?q=sqlite datasette-sqlite-fts4: https://datasette.io/plugins/datasette-sqlite-fts4 ; Full-Text Search with SQLite datasette-ripgrep: "deploy a regular expression search engine for your source code": https://github.com/simonw/datasette-ripgrep Seeing as there's already a JSONLD @context (schema) for code.json, CSVW as JSONLD and/or YAMLLD would be an easy way merge Linked Data graphs of tabular data: https://github.com/semantalytics/awesome-semantic-web#csvw A GitHub Action would run regularly, fetch each code.json, save each to a git repo, and then upsert each into a SQLite database to be published with e.g. datasette or datasette-lite.
In Code-gov: A collection point for all Code.gov repositories
I'm thrilled to see my project (https://mockoon.com) being part of this first cohort! It's a great opportunity but it's also good for open-source projects in general. It can only contribute to make our industry more sustainable. Here is the list of the 20 projects that have been selected: - https://github.com/analogjs/analog: Analog is a fullstack meta-framework to build applications and websites with Angular - https://github.com/Atri-Labs/atrilabs-engine: The Python web framework to build production-grade apps - https://github.com/bigskysoftware/htmx: Makes AJAX, Web Sockets, etc. available directly in HTML - https://github.com/code-hike/codehike: Tools for building all types of code walkthroughs: blogs, docs, slides, tutorials, etc. - https://github.com/DioxusLabs/dioxus: Friendly React-like GUI library for desktop, web, mobile, and mor
Read the rest
e, written in Rust. - https://github.com/EddieHubCommunity/LinkFree: Connecting with your audience with a single link, showcasing your content and projects. - https://github.com/FashionFreedom/Seamly2D: Design CAD to democratize and de-centralize fashion design & production. - https://github.com/fastai/nbdev: Increase developer productivity by 10x with a new exploratory programming workflow. - https://github.com/formbricks/formbricks: Open-source Qualtrics alternative - https://github.com/GyulyVGC/sniffnet: Cross-platform application to comfortably monitor and analyse network traffic - https://github.com/JessicaTegner/pypandoc: Pypandoc provides a thin wrapper for pandoc, a universal document converter. - https://github.com/mockoon/mockoon: Mockoon is the easiest and quickest way to design and run mock REST APIs. - https://github.com/nuxt/nuxt: An intuitive framework for building web applications, built for the edge. - https://github.com/responsively-org/responsively-app: A dev-tool for web developers that aid in faster responsive web page development. - https://github.com/simonw/datasette: An open source multi-tool for exploring and publishing data - https://github.com/spyder-ide/spyder: The scientific Python development environment - https://github.com/strawberry-graphql/strawberry: A GraphQL library for Python that leverages type annotations - https://github.com/termux/termux-app: A terminal emulator for Android that provides 2000+ linux packages to code on phone - https://github.com/TimothyStiles/poly: Poly is a fast, well tested Go package for engineering organisms. - https://github.com/trpc/trpc: End-to-end typesafe APIs made easy. Never write another API contract again. Edit: formatting and updated repo links
In GitHub Accelerator: our first cohort and what’s next

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