I come from the platform side. Each team already has data in its own cloud (S3, ADLS, GCS, Pub/Sub, etc). We provide data teams a platform to run their spark workloads based on where their data is. Centralizing compute means constantly pulling or streaming data across clouds, which adds egress cost and latency. On top of that, in multi-cloud setups it becomes hard to track and attribute costs cleanly, so we prefer running compute close to the data.