Advanced-driving programs increasingly compete on how quickly they can turn field data into a safer model. Fragmented storage and repeated data movement slow that loop even when compute capacity is abundant.
Data spans the full development cycle
Sensor collection, cleansing, labeling, model training, simulation, validation, and fleet feedback all generate different access patterns. The slowest data handoff can set the pace for the entire program.
Fragmentation creates an I/O wall
Separate systems for ingestion, file access, object workflows, and archive increase migration work and operational complexity. GPU resources may wait while data is copied, converted, or searched elsewhere.
Build one governed data plane
A multi-protocol, scale-out platform keeps datasets in a shared namespace and applies lifecycle policy as their value changes. That architecture shortens the path from a newly captured edge case to the next training run.
