Random perspective: I think the author is unintentionally speaking of a future not quite here yet & specifically with AI integrated into applications & their infrastructure. Essentially the need for low data gravity with how AI works today in viable business domains.
For large processing of data in general requests (maybe like a traditional SQL query) AI might typically require the retrieval data to map to vector formats. But it's not applicable to add those to RDBs (you're not doing any sql upfront, retrieval will be too slow), nor compute those ad-hoc (business app use cases, not general CV or NLP models, nor analytics).
AI/ML inputs/vectors need to exist somewhere where it is fast access vs abstracted software over that access; so using embedded dbs (leveldb, rocks, badger, lmdb, etc). Anecdotally, I get top performance over 8-24 physical ssd attachments (TBs of data), nvme raid-0 single mount on individual nodes, where we need to gather anywhere from 1 - potentially 10s of thousands of vectors every request. It's never more performant to distribute that and nothing is really gained with persistence. Today's platforms are somewhat geared for fast recovery (though writing TB's of data isnt simple, you can model automation & scale horizontally). Still, app platform infrastructure isn't really geared for AI application architectures like this. E.g., Kubernetes is too abstracted, but I imagine Google/GCP will, intentionally for AI workloads, enable CSI drivers and more robustness around Local SSDs on GKE.
For large processing of data in general requests (maybe like a traditional SQL query) AI might typically require the retrieval data to map to vector formats. But it's not applicable to add those to RDBs (you're not doing any sql upfront, retrieval will be too slow), nor compute those ad-hoc (business app use cases, not general CV or NLP models, nor analytics).
AI/ML inputs/vectors need to exist somewhere where it is fast access vs abstracted software over that access; so using embedded dbs (leveldb, rocks, badger, lmdb, etc). Anecdotally, I get top performance over 8-24 physical ssd attachments (TBs of data), nvme raid-0 single mount on individual nodes, where we need to gather anywhere from 1 - potentially 10s of thousands of vectors every request. It's never more performant to distribute that and nothing is really gained with persistence. Today's platforms are somewhat geared for fast recovery (though writing TB's of data isnt simple, you can model automation & scale horizontally). Still, app platform infrastructure isn't really geared for AI application architectures like this. E.g., Kubernetes is too abstracted, but I imagine Google/GCP will, intentionally for AI workloads, enable CSI drivers and more robustness around Local SSDs on GKE.