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):
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/
Analytics with type-safe raw SQL (including DuckDb’s awesome extensions) is pure gold:
https://github.com/manifold-systems/manifold/blob/master/doc...
Over the years I've seen anecdotes here on HN that DuckDB crashes often for several people. Is this still an issue for anyone?
The actual slides are linked from the intro-text:
https://github.com/DBatUTuebingen/DiDi
Unfortunately it does not seem that there are lecture videos.
thank you! Learned why DuckDB is named this way
Am I missing something or is the content empty?
https://github.com/DBatUTuebingen/DiDi
Thank you, I didn't realize all of the course counted as "slides and auxiliary material" haha
edit: Really great stuff in here. Every day at work I think about how much I love DuckDB