What's the difference between this, and Datasette which has been in development multiple years by the Django co-creator?
Sorry if it's a dumb question but I skimmed the docs and sample and it looks similar + didn't see any mention:
https://github.com/simonw/datasette
In Roapi: An API Server for Static Datasetssimonw
datasette
Python2017–2026
simonw / datasette
datasette
An open source multi-tool for exploring and publishing data
36Distinct people
77Mentions
1Explicit recommenders
11.6KGitHub stars
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.
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Also in these discussions
- simonw/sqlite-utils · linked in 6 of the same discussions
- jeffknupp/sandman2 · linked in 3 of the same discussions
- simonw/csvs-to-sqlite · linked in 3 of the same 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, 11:34:28 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.
* 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
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| 2017 | 2017-01-01 00:00:00 UTC | 2018-01-01 00:00:00 UTC | 1 | 0 | 1 |
| 2018 | 2018-01-01 00:00:00 UTC | 2019-01-01 00:00:00 UTC | 6 | 0 | 6 |
| 2019 | 2019-01-01 00:00:00 UTC | 2020-01-01 00:00:00 UTC | 5 | 1 | 4 |
| 2020 | 2020-01-01 00:00:00 UTC | 2021-01-01 00:00:00 UTC | 21 | 1 | 20 |
| 2021 | 2021-01-01 00:00:00 UTC | 2022-01-01 00:00:00 UTC | 11 | 2 | 9 |
| 2022 | 2022-01-01 00:00:00 UTC | 2023-01-01 00:00:00 UTC | 12 | 0 | 12 |
| 2023 | 2023-01-01 00:00:00 UTC | 2024-01-01 00:00:00 UTC | 5 | 0 | 5 |
| 2024 | 2024-01-01 00:00:00 UTC | 2025-01-01 00:00:00 UTC | 5 | 0 | 5 |
| 2025 | 2025-01-01 00:00:00 UTC | 2026-01-01 00:00:00 UTC | 3 | 0 | 3 |
| 2026 (partial) | 2026-01-01 00:00:00 UTC | 2027-01-01 00:00:00 UTC | 8 | 1 | 7 |
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 GitHubWhat Hacker News says
11 sourcesI'm working on an open-source desktop app [0] similar to Jupyter but more oriented toward non-data-science developer workflows like querying/joining data from multiple databases and scripting and graphing for internal reporting.
Another similar tool, but maybe more data science focused, is Simon Willison's Datasette [1].
[0] https://github.com/multiprocessio/datastation
[1] https://github.com/simonw/datasette
In JupyterLab Desktop App now available"Issues are independent. Properly broken down, work in a project has dependencies"
A trick I use a lot is to open a "tracking issue" and then link to it from other dependent issues by mentioning its #ID in those issue's descriptions.
The tracking issue automatically shows the open/close status of those other issues, which means I can see at a glance if all of the dependent issues have been completed.
Here's an example: https://github.com/simonw/datasette/issues/1105
It's not nearly as sophisticated as what you're looking for in terms of date estimates and critical paths, but it does work well for light-weight dependency tracking.
In GitHub Issues-only project managementHuge +1 for Sentry - and their free plan is easily generous enough for projects that are just getting started.
I considered using Link headers for pagination in Datasette and got some useful pushback: https://github.com/simonw/datasette/issues/782#issuecomment-...
> It has a lot of strikes against it: poor discoverability, new developers often don’t know how to use them, makes CORS harder, makes it hard to use eg with JQ, needs ad hoc specification for each bit of metadata, etc.
In PAGNIs: Probably Are Gonna Need ItsI've found writing tests against these endpoints to be pretty easy - I populate the associated database table with one record more than the default page size and then run a test that retrieves the full result and checks that the expected records are present. No need to run tests that generate 100MB responses every time - though I will do a few manual tests of large responses to check that RAM usage is as-expected.
Here's my test in Datasette for the streaming CSV response - I already have a table with 1001 items in it setup for another test: https://github.com/simonw/datasette/blob/0.57.1/tests/test_c...
In Notes on streaming large API responsesI use documentation systems that publish the documentation from the repo to a website. Most of my projects use Sphinx and reStructuredText for this, but I recently tried MyST (Markdown for Sphinx) and I like that a lot.
Some examples:
- https://docs.datasette.io serves documentation from https://github.com/simonw/datasette/tree/main/docs - which has documentation unit tests here: https://github.com/simonw/datasette/blob/main/tests/test_doc...
- https://sqlite-utils.datasette.io/ serves from https://github.com/simonw/sqlite-utils/tree/main/docs - unit tests here: https://github.com/simonw/sqlite-utils/blob/main/tests/test_...
- https://django-sql-dashboard.datasette.io/ serves from markdown in https://github.com/simonw/django-sql-dashboard/tree/main/doc... - I don't have documentation unit tests for that yet
Those three are all hosted on h
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ttps://www.readthedocs.org but I've also used this trick on web app projects that host their own documentation deployed as part of the build process.
This looks pretty efficient. Some chains can be interacted with without e.g. web3.js? LevelDB indexes aren't SQLite.
Datasette is one application for views of read-only SQLite dbs with out-of-band replication. https://github.com/simonw/datasette
There are a bunch of *-to-sqlite utilities in corresponding dogsheep project.
Arrow JS for 'paged' browser client access to DuckDB might be possible and faster but without full SQLite SQL compatibility and the SQLite test suite. https://arrow.apache.org/docs/js/
https://duckdb.org/ :
> Direct Parquet & CSV querying
In-browser notebooks like Pyodide and Jyve have local filesystem access with the new "Filesystem Access API", but downloading/copying all data to the browser for every run of a browser-hosted notebook may not be necessary.
https://web.dev/file-system-access/
In Hosting SQLite databases on GitHub Pages or any static file hosterNot "in lieu", but I have found SQLite to be a good companion to pandas. pandas is good when you want to lend some easily navigable structure to your data without leaving python, but becomes cumbersome (it feels cumbersome esp if you know SQL) when you want to do some serious processing. At such times, I just dump my data into a SQLite DB, and from then on my code has SQL queries to it, which I find are far more readable than corresponding pandas statements (also easier for the reviewer).
Another advantage you get is if you wanted to look at these intermediate data, you don't need to run your code in debug mode and view the dataframe at a breakpoint - you can use something like datasette[1] or a standard SQLite DB viewer.
So a function that does complex data processing has approximately this structure my code:
(1) <non-pandas python cod
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e to fetch data, other "normal" stuff>
(2) <pandas code to do basic navigation, filtering, grouping etc>
(3) <dump to SQLite, followed by SQL queries for the really heavy stuff>
(4) <back to python, possibly pandas, to return results, write to a file, plot etc>
Step (3) used to be pandas for me before, but depending on how complex your operations are this can become hard to read and/or review.
[1] https://github.com/simonw/datasette - datasette doesnt replace a standard DB IDE, but is a very good lightweight alternative to one if don't intend to perform updates/inserts directly on a table.
With no sense of overstatement here, SQLite is one of my favorite creations in the entire world, so I have a bunch of links some of you might find interesting if you want to dig further:
https://github.com/sql-js/sql.js - SQL.js lets you run SQLite within a Web page as it's just SQLite compiled to JS with Emscripten.
https://litestream.io/blog/why-i-built-litestream/ - Litestream is a SQLite-powered streaming replication system.
https://sqlite.org/lang_with.html#rcex3 - you can do graph-style queries against SQLite too (briefly mentioned in the article).
https://github.com/aergoio/aergolite - AergoLite is replicated SQLite but secured by a blockchain.
https://github.com/simonw/datasette - Datasette (mentioned at the very end of the OP article) is a tool for offering up an SQLite database as a Web accessible service - you can do queries, d
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ata analysis, etc. on top of it. I believe Simon, the creator, frequents HN too and is a true SQLite power user :-)
https://dogsheep.github.io/ - Dogsheep is a whole roster of tools for doing personal analytics (e.g. analyzing your GitHub or Twitter use, say) using SQLite and Datasette.
Redesign default JSON format in preparation for Datasette 1.0
1 points · Open discussionDatasets.io: An open source multi-tool for exploring and publishing data
3 points · Open discussion