AI data storage and protection
A reference design that separates working datasets, shared data services and protected copies, aligning storage and connectivity with AI workloads.
Architecture and delivery scope
Data inventory — record current volume, growth, file sizes, read/write patterns and the number of simultaneous jobs.
Storage tiers — distinguish active working data, shared retained data and backup copies; choose interfaces and service boundaries for each.
Platform selection — assess catalog storage servers and drives with exact part numbers. Drive compatibility, firmware, endurance and software support require confirmation.
Network path — measure transfers between ingestion, storage and compute nodes; choose uplinks and cabling after identifying the bottleneck.
Protection plan — agree recovery point and recovery time objectives, retention, backup isolation and operational ownership.
Capacity schedule — show raw and usable capacity separately, including protection overhead, operational reserve and planned growth.
Acceptance — test concurrent reads and writes with representative data, then demonstrate a restore procedure. Performance depends on the final hardware and software configuration.