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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

21 sources
I'm working on improvements related to this at the moment. Currently you need to run Datasette against existing SQLite databases when it starts up. Plugins like https://datasette.io/plugins/datasette-upload-csvs can be used to let people add new tables to those existing databases, but there isn't a mechanism to add a whole new database file without restarting the server. In the next release I hope to be able to support pointing Datasette at a directory and having any new SQLite files that are added to that directory automatically show up in the interface without having to restart the server. You can follow this issue for updates: https://github.com/simonw/datasette/issues/417
In Datasette.io, an official project website for Datasette
This is the new Datasette project website - the open source project is three years old now but I launched this website yesterday. Up until now there's just been the GitHub repo at https://github.com/simonw/datasette and the project documentation at https://docs.datasette.io/ The https://datasette.io/ site is running on Datasette itself, with a bunch of custom templates. If you're interested in seeing how it works the source code (and the GitHub Actions that build and deploy the underlying database) can be found here: https://github.com/simonw/datasette.io
In Datasette: An open source multi-tool for exploring and publishing data
I've been using GitHub issues for this for a couple of years, and I really like it. Any time I run into a bug, no matter how minor, I open an issue for it. I then use comments on that issue to track every step I use to fix it - adding screenshots, pasting in blocks of command-line output, linking to debugging tools I use etc. I usually end up with an issue thread with a dozen or so comments, all by me. I often refer back to old issue threads to remind myself how I debugged something. Here's an example from yesterday: https://github.com/simonw/datasette/issues/1124
In Why I keep a personal log of bugs
I've actually prototyped this recently, but never got around to packaging it up into a library. There are a few neat adjacent things that it can be used for (e.g. opening big SQLite files on disk without reading it all to memory), building something like Datasette (https://github.com/simonw/datasette) that can run queries on data hosted as static files, or for (as you suggested) using SQLite in a browser with persistence. For that particular use case, there's a bit of complexity related to mutexes and stuff in trying to prevent simultaneous browser tabs doing write operations from corrupting the database.
In SQL.js: SQLite Compiled to JavaScript
A giant plant as in someone from their team planning this out and then planting this as a comment..? simonw wrote Datasette[1] which makes extensive use of SQLite and acts as a viewer, allowing you to create interactive websites and APIs from arbitrary databases. He'd be a very long term plant and it instead seems far more likely he's interested in the possibilities of DuckDB compatibility within the context of Datasette and other similar projects. [1]: https://github.com/simonw/datasette
In DuckDB – An embeddable SQL database like SQLite, but supports Postgres features

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