Polars would be better in some ways for sure. It was in one of my early prototypes. What put me off was that I was essentially designing my own database which I didn't trust as much as something like DuckDB.

Polars would let me have a lot of luxuries that are lost at the boundaries between my application and DuckDB, but those are weighed in the tradeoffs I was talking about. I do a lot of parsing at the boundaries to ensure data structures are sound, and otherwise DuckDB is enforcing strict schemas at runtime which provides as much safety as a dataset's schema requires. I do a lot of testing to ensure that I can trust how schemas are built and enforced as well.

Things like foreign keys, expressions that span multiple tables effortlessly, normalization, check constraints, unique constraints, and primary keys work perfectly right off the shelf. It's kind of perfect because the spec I'm supporting is fundamentally about normalized relational data.

Another consideration was that while Polars is a bit faster, we don't encounter datasets that require more speed. The largest dataset I've processed, including extensive transformations and complex validations (about as complex as they get in this spec), takes ~3 seconds for around 580k rows. That's on an M1 Max with 16GB of RAM, for what it's worth.

Our teams have written countless R scripts to do the same work with less assurance that the outputs are correct, having to relearn the spec each time, and with much worse performance (these people are not developers). So, we're very happy with DuckDB's performance despite that Polars would probably let us do it faster.

Having said that, if someone built the same project and chose Polars I wouldn't think they were wrong to do so. It's a great choice too, which is why your question is a good one.