Vineyard addresses on sharing "distributed abstracted immutable data" in memory, e.g., dask[1] generates a distributed dataframe (since the size of data cannot be fit into a single machine), and feed it to tensorflow as features for distributed training. Vineyard aims at sharing the "DataFrame" between these two engines, by memory sharing.

+ feather: as well as arrow, are focused on interoperable data stroage format. feather and arrow define a IPC serailziation schema for common data structures, e.g., tensor and DataFrame. Rather than do serailization for zero-copy (when possible) deserialization, vineyard organized data as a "metadata" and a set of "blobs", the metadata decides how to interpret those blobs and blobs are shared in a zero-copy fashion. Moreover, vineyard could manages large that cannot be fit into a single machine as a "GlobalObject" and global objects can be shared efficiently as well.

+ fst: fst is also a serailization/stroage format, but vineyard is not such a thing.

+ diskframe: diskframe is similar to fst, and works for data that cannot be fit into memory. Vineyard shares in-memory data that cannot be fit into a single machine between different engines (might be implemented in different languages).

+ duckdb: vineyard is not a SQL execution engine.