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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, 10:02:05 AM 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
There are lots of apps that do database introspection. Some also generate forms on the fly, but eventually it's necessary to: specify a forms widget for a particular field because SQL schema only describes the data and not the UI; and specify security authorization restrictions on who can create, read, update, or delete data. And then you want to write arbitrary queries to filter on columns that aren't indexed; but it's really dangerous to allow clients to run arbitrary SQL queries because there basically are no row/object-level database permissions (the application must enforce row-level permissions). Datasette is a great tool for read-only database introspection and queries of SQLite databases. https://github.com/simonw/datasette Sandman2 generates a REST API for an arbitrary database. https://github.com/jeffknupp/sandman2 You can gener
Read the rest
ate Django models and then write admin.py files for each model/table that you want to expose in the django.contrib.admin interface. There are a number of apps for providing a GraphQL API given introspection of a database that occurs at every startup or at runtime; but that doesn't solve for row-level permissions (or web forms) If you have an OpenAPI spec for the REST API that runs atop The database, you can generate forms ("scaffolding") from the OpenAPI spec and then customize those with form widgets; optionally with something like json-schema. It's not safe to allow introspected CRUD like e.g. phpMyAdmin for anything but development. If there are no e.g. foreign-key constraints specified in the SQL schema,a blindly-introspected UI very easily results in database corruption due to invalid foreign key references (because the SQL schema doesn't specify what table.column a foreign key references). Django models, for example, unify SQL schema and forms UI in models.py; admin.py is optional but really useful for scaffolding (such as when you're doing manual testing because you haven't yet written automated tests) https://docs.djangoproject.com/en/2.2/ref/contrib/admin/#mod...
In Ask HN: Is there a CRUD front end for databases (especially SQLite)?

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