I'm not really familiar of how datasets are managed by them, but all of the table formats (iceberg, delta and hudi) support appending and some form of "merge-on-read" deletes that could help with this use case. Instead of always fully replacing datasets on each dump, more granular operations could be done. The issue is that this requires changing pipelines and some extra knowledge about the datasets itself. A fun idea might involve taking a table format like iceberg, and instead of using parquet to store the data, just store the column data with the metadata externally defined somewhere else. On each new snapshot, a set of transformations (sorting, spiting blocks, etc) could be applied that minimizes that the potential byte diff between the previous snapshot.

If the sharding key matches (or is a subset of) a join or group-by key, then identical values are local to a single shard, which can be processed independently.

This type of thing is typically done at large granularity (eg one shard per MPP compute node), but there are also benefits down to the core or thread level.

Another tip is that if no shard key is defined, hash the whole row as a default.