Author here. I wouldn't argue SQL or duckdb is _more_ testable than polars. But I think historically people have criticised SQL as being hard to test. Duckdb changes that.

I disagree that SQL has nothing to do with fast. One of the most amazing things to me about SQL is that, since it's declarative, the same code has got faster and faster to execute as we've gone through better and better SQL engines. I've seen this through the past five years of writing and maintaining a record linkage library. It generates SQL that can be executed against multiple backends. My library gets faster and faster year after year without me having to do anything, due to improvements in the SQL backends that handle things like vectorisation and parallelization for me. I imagine if I were to try and program the routines by hand, it would be significantly slower since so much work has gone into optimising SQL engines.

In terms of future proof - yes in the sense that the code will still be easy to run in 20 years time.

> I disagree that SQL has nothing to do with fast. One of the most amazing things to me about SQL is that, since it's declarative, the same code has got faster and faster to execute as we've gone through better and better SQL engines.

Yeah, but SQL isn't really portable between query all query engines. You always have to be speaking the same dialect. Also, SQL isn't the only "declarative" dsl, polars's lazyframe api is similarly declarative. Technically Ibis's dataframe dsl also works as a multi-frontend declarative query language. Or even substrait.

Anways my point is that SQL is not inherently a faster paradigm than "dataframes", but that you're conflating declarative query planning with SQL.