One thing that high-performance polars/arrow enables is to build ETL without data sharding because in most cases, all your data fits into one node. Jupyter and local scripts become sufficient and you avoid a lot of complexity.
We hesitated a lot before mentioning ETL in the OSS project windmill [1] but since we support any python including polars, passing data between steps through shared folders and since polars is blazingly fast, it is a suprisingly good system for ETL waiting for the need of using spark/materialize directly.