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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, 12:45:45 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

12 sources
That demo link is so impressive: I just watched my browser DevTools and it loaded 23MB of data in order to run that query against that 28GB database. I really need to dig in and figure out how Datasette (and Datasette Lite) can work better for this. I think this issue might help - the ability to turn off row counts entirely: https://github.com/simonw/datasette/issues/1818 (I noticed that trying to access the table directly seems to suck in a LOT of data, presumably because it's trying to calculate a count across the whole table?)
In SQLite in the browser with WASM/JS
OK, I really like how you're framing this in terms of the first-run experience. Running "brew/pip install datasette" isn't much help if you don't have a SQLite database already. So now you need to learn to use "sqlite-utils" or similar too to create one... by which point I could absolutely see most people having dropped off already. I had a feature idea relating to this a while back which I should bump up the list: https://github.com/simonw/datasette/issues/1160 I opened a new issue based on this conversation here: https://github.com/simonw/datasette/issues/1845
In Shot-scraper: Automating screenshots for documentation
I would recommend SQLite + a nice usable interface tool like datasette https://github.com/simonw/datasette Or SQLitebrowser https://sqlitebrowser.org/ (There are other generalized tools, search around to see if there are more suitable.) You can store whole files, in addition to searchable text fields , and the single file is easier to share or turn into a more sophisticated “app” when the time is right.
In Ask HN: What's the best way to create a database for legal document clauses?
That's something I'm still figuring out. In particular, I want plugins that add extra content to pages to be able to collaborate with each other. I have an open issue for that here: https://github.com/simonw/datasette/issues/1191 I'm not too worried about extra template vars overrides though - that will only be a problem if two plugins accidentally use the same variable name. Maybe I should encourage plugins to use a namespace based on their name in the documentation though.
In Architecture Notes: Datasette
> * Which dialect for querying worked the best for you? If this is SQL dialects, I am partial to both the Postgres JSON/JSONB operators, SQLite has a solid JSON implementation too. > * Which tools do you enjoy the most? This could be for any reason: convenience of install/availability, expressivity, raw speed, other niche features, etc. "Datasette" (from Django co-creator) can take tabular data (SQLite, CSV, JSON, etc) and generate a REST/GraphQL API with visualization tools from it: https://github.com/simonw/datasette From the same author, "sqlite-utils" generate SQLite table definitions and rows from similar: https://github.com/simonw/sqlite-utils I find this useful outside of SQLite because the syntax is similar across SQL databases. Great way to bootstrap a DB definition from existing datasets. "Pipe JSON (or CSV or TSV) directly
Read the rest
into a new SQLite database file, automatically creating a table with the appropriate schema" > * What sort of JSON "meta-formats" are the most important/common for you? E.g. in a file you could have object-per-line, object-of-arrays, array-of-objects, or in an SQL context you could have object-per-row or object-of-arrays-as-table, etc). I'd love to hear about others that are important to you. For file, either JSONL/NDJSON (JSON object per line) if it's large, or a single array of objects if it's small. In a SQL context, object-per-row undoubtedly > * What do you feel is the biggest gap/missing feature in currently available tools? Don't have anything useful on this one unfortunately, but I'm not an analytics/data science person =(
In The fastest tool for querying large JSON files is written in Python (benchmark)
I was planning to implement that a while ago, but I ran into a MASSIVE blocker on that. It turns out Cloudflare - the CDN I use for a bunch of my projects (and often recommend to others) - ignores the "Vary: Accept" HTTP header in most cases! This means you can't safely put a URL that varies on Accept behind a Cloudflare cache - because there's a risk that the cached copy will be stored in an unexpected format, in a way that affects future traffic. I'm really annoyed about this. Notes on that here: https://github.com/simonw/datasette/issues/1534
In I invented a “dash encoding”, a new encoding scheme for URL paths

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