mrtimo

This reminds of of this tool [1]. It's a wasm that shows "overrepresented baby names". Choose a state, choose a time frame, it will tell you which names were given more in that state compared to all other states. Ever heard of an "Alaska Name" or an "Idaho Name"... find them with this tool.

[1] https://docs.malloydata.dev/malloyyo-babynames/over-represen...

I can use pandas to clean a dataset, but each cleaning task is usually one line of code. OTOH, With DuckDB with one SQL statement I can replace 40+ lines of polars/pandas. You may reply, SQL isn't as easy to understand! Fair point, it's a declarative language... which is why I use Malloy. Malloy is to TypeScript as Javascript is to SQL. Malloy is much easier to read and write (just as TypeScript is) because it has a built in semantic model -- all the joins, measures, and dimensions are done in one place.

Here is an example [1] of visualizing college football games. Here are all the queries, and semantic model that power all the visualizations [2] Here is the AI generated typescript/react that does the visualizations [3]. The Malloy ecosystem has Malloyyo and Publisher which are replacements for PowerBI and Tableau and Looker. Here is another example for visualizing global trade [4].

[1] - https://mrtimo.github.io/cfb-games/games-2026.html?week=Week... [2] - https://github.com/mrtimo/cfb-games/blob/main/drives.malloy [3] - https://github.com/mrtimo/cfb-games/blob/main/dashboards/gam... [4] - https://tradeexplorer.org/

Malloy is an alternative to dbt, they have a similar product called Malloyyo [1], and Publisher [3], you can also the chat with HN data demo they have [4].

DBTCharts says you can serve charts locally, but it seems they want you to use their hosting service in production. Whereas, Malloyyo / Publisher are free to use anywhere. If you like college football checkout how I visualize drive data in [2], made with Malloyyo.

[1] - https://github.com/malloydata/malloyyo [2] - https://mrtimo.github.io/cfb-games/games-2026.html?%24SEASON... [3] - https://github.com/malloydata/publisher [4] - https://community.credibledata.com/hackernews

It said I clicked on water a few times when I had not... Maybe it was a canal? Very cool.

Check out malloyyo on github

I've moved from python/polars/pandas to DuckDB and have not looked back

The article mentions a dataset of names given to people in the USA. This is a fun name explorer WASM based on that data. 811 people in the USA have named their baby 'Diesel': https://docs.malloydata.dev/malloyyo-babynames/name_explorer...

you should checkout https://github.com/malloydata/malloyyo - it's great at what you trying to do.

Thanks for sharing, and thanks for making the video you shared. A couple thoughts. Is PostGIS king in this area? I've been liking duckDB as it does not require a server. Is there a good alternative to PostGIS in the duckDB world? Maybe just plain GeoParquet files read in through duckDB?

In the example in your video are any special GEOMETRY functions being used in the underlying SQL? Or, could your data just have been in plain postgres?

A similar product GeoSQL is Malloy which puts a semantic layer on top of your data for better LLM understanding. Malloyyo gives you an MCP server for precise and auditable interaction with your data.

Thanks for sharing all this. Where is the submission page on claude / openAI to submit MCP servers to their directories?

Question: I have an MCP server that is working well with Auth (Google Only), running on vercel. What benefit would I get from running it on manufact?

On monetizing my MCP... How do the different MCP "Stores" handle this? Do some take a cut? Or are they agnostic? Does manufact help with monetization?

Can it be run as a static site(github pages) ?

This is awesome. Is your code open source? It would be cool to make a textbook for SQL in this format.

If you are a data scientist or do anything with data... duckdb is like a swiss army knife. So many great ways it can help your workflow. The original video from CMU in 2020 [1] is a classic. Minutes 3-8 present a good argument for adding duckdb to your data cleaning/processing workflow.

And if you want to add a semantic layer on top of data, Malloy [2] is my favorite so far (it has duckdb built in):

[1]: https://www.youtube.com/watch?v=PFUZlNQIndo [2]: https://docs.malloydata.dev/documentation/

Nice work. Have you blogged about how you built it?

Spotify says it will take 30 days for the export... it really only takes about 48 hours if I remember correctly. While you wait for the download here is an example listening history exploration in malloy - I converted the listening history to .parquet: https://github.com/mrtimo/spotify-listening-history

What I love about duckdb:

-- Support for .parquet, .json, .csv (note: Spotify listening history comes in a multiple .json files, something fun to play with).

-- Support for glob reading, like: select * from 'tsa20*.csv' - so you can read hundreds of files (any type of file!) as if they were one file.

-- if the files don't have the same schema, union_by_name is amazing.

-- The .csv parser is amazing. Auto assigns types well.

-- It's small! The Web Assembly version is 2mb! The CLI is 16mb.

-- Because it is small you can add duckdb directly to your product, like Malloy has done: https://www.malloydata.dev/ - I think of Malloy as a technical persons alternative to PowerBI and Tableau, but it uses a semantic model that helps AI write amazing queries on your data.

Nice work Andy. I'd love to hear about semantic layer developments in this space (e.g. Malloy etc.). Something to consider for the future. Thanks.

Me too

I'm using DuckDB WASM on github pages. This will take about 10 seconds to load [1] and shows business trends in my county (Spokane County). This site is built using data-explorer [2] which uses many other open-source projects including malloy and malloy-explorer. One cool thing... if you use the UI to make a query on the data - you can share the URL with someone and they will see the same result / query (it's all embedded in the URL).

[1] - https://mrtimo.github.io/spokane-co-biz/#/model/businesses/e... [2] - https://github.com/aszenz/data-explorer

DuckDB can read JSON - you can query JSON with normal SQL.[1] I prefer to Malloy Data language for querying as it is 10x simpler than SQL.[2]

[1] - https://duckdb.org/docs/stable/data/json/overview [2] - https://www.malloydata.dev/

I have experience with duckDB but not databricks... from the perspective of a company, is a tool like databricks more "secure" than duckdb? If my company adopts duckdb as a datalake, how do we secure it?

Love this! Here is a similar product: https://sql-workbench.com/

Based on this comment, you might enjoy the Malloy data language. It compiles to SQL and also have an open source explorer to make filters like what you are saying easy.

It’s 2025. Let’s separate storage from processing. SQLite showed how elegant embedded databases can be, but the real win is formats like Parquet: boring, durable storage you can read with any engine. Storage stays simple, compute stays swappable. That’s the future.

Same. But, I use Malloy which uses duckdb to query data stored in hundreds of parquet files (as if they were one big file).

I agree with this 100%. The creator of duckdb argues that people using pandas are missing out of the 50 years of progress in database research, in the first 5 minutes of his talk here [1].

I've been using Malloy [2], which compiles to SQL (like Typescript compiles to Javascript), so instead of editing a 1000 line SQL script, it's only 18 lines of Malloy.

I'd love to see a blog post comparing a pandas approach to cleaning to an SQL/Malloy approach.

[1] https://www.youtube.com/watch?v=PFUZlNQIndo [2] https://www.malloydata.dev/