Ah I understand what you are saying. Not trying to aggressively make you try Fugue out, I appreciate the comments and just want to clarify some stuff. There is

Ah I understand what you are saying. Not trying to aggressively make you try Fugue out, I appreciate the comments and just want to clarify some stuff. There is no transpiling, we think that is too magical. It's more of routing to the appropriate functions/method. For example, applying the Pandas function per partition. We use Pandas UDF under the hood when it makes sense. Sometimes, we use map partitions as well, it depends on what the user wants to do. So we simplify usage around these methods with minimal overhead (we benchmarked it). Fugue can be adopted as minimally as needed so if you feel the need to write native Spark code or tune Spark, we don't block that from the user. The older Fugue interface was bad in allowing this, so we reworked Fugue to have a suite of standalone functions compatible with Spark/Dask/Ray/Pandas DataFrames. Our opinion (which is totally fine if you disagree), is that most workloads don't really specific features of Spark. There are times when it makes sense to write native Spark code for sure, and for those Fugue won't be a good fit. Our job is to make sure it's not an all or nothing thing that requires the user to make compromises.