This is intended for analytical workloads where you're often doing things that can benefit from vectorization (like SIMD). It's much faster to SUM(X) when all values of X are neatly laid out in-memory.
It also has the added benefit of eliminating serialization and deserialization of data between processes - a Python process can now write to memory which is read by a C++ process that's doing windowed aggregations, which are then written over the network to another Arrow compatible service that just copies the data as-is from the network into local memory and resumes working.
It also has the added benefit of eliminating serialization and deserialization of data between processes - a Python process can now write to memory which is read by a C++ process that's doing windowed aggregations, which are then written over the network to another Arrow compatible service that just copies the data as-is from the network into local memory and resumes working.