Duckdb supports loading and saving to zstd for all it's base loading/saving formats csv/tsv/json/jsonlines, but, for good or bad, those are solid compression.

Under most r/w workloads, using parquet/lance/vortex/native-duckdb, with their built-in columnar compression will result in more performance AND space savings. Non-solid compression. Then, the query engine can push down your query predicate to a column row group level, instead of forcing it to decompress the entire dataset to operate.

Practical example: duckdb has syntax - https://duckdb.org/docs/lts/data/multiple_files/overview - to glob multiple files at once, but that really only works if you're applying push down query predicates instead of re-decompressing your entire data set per SELECT. I would say for most dataset, even 20%+ size is worth not having to decompress (or even download!) the entire dataset, to figure out if something fits the predicate.

After all, if you have to download and decompress the dataset back again to operate, then the "space savings" are gone.