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.
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):
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
-- 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. Edit: Malloy makes SQL 10x easier to write because of its semantic nature. Malloy transpiles to SQL, like Typescript transpiles to Javascript.
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).
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?
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.
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.