Records-lake designers need fast scratch space for training checkpoints and separately shared datasets many jobs can read. How should they map block versus file storage roles?

Mapping block volumes to training scratch/checkpoints and file shares to shared multi-job datasets correctly assigns storage roles in an AI data path. Block storage commonly delivers high-performance, job-local scratch and checkpoint volumes; file shares provide concurrent read access to common datasets across many training jobs. Records-lake designers should match access pattern to storage type rather than forcing one medium for every need.

Using only email attachments as the training data plane fails because email is not a high-throughput, multi-job training storage architecture. Storing all hot checkpoints exclusively on public social media CDNs fails conceptually on performance, control, confidentiality, and suitability—CDNs are not municipal training scratch. Eliminating storage entirely and keeping tensors only in switch TCAM fails because switch TCAM is not a training checkpoint/dataset store and cannot replace block/file roles.

Exam caveat: shared file datasets still need permissions and caching strategy so one noisy job cannot corrupt or starve others; block checkpoint volumes need backup/replication policy. Operational check: provision block volumes for scratch/checkpoints, expose file shares for shared corpora, measure checkpoint I/O, and reject email/CDN/TCAM distractors for the training data plane.