Generative AI workloads demand a fundamentally different approach to enterprise data infrastructure. Unlike traditional analytics pipelines, large language model training and inference require extreme throughput, low-latency data access at massive scale, and the ability to ingest unstructured data from dozens of sources simultaneously.
This resource outlines the architectural principles organizations must establish before deploying production-grade generative AI — including high-performance storage tiers optimized for GPU clusters, data lakehouse patterns that unify structured and unstructured datasets, and governance frameworks that ensure model training data is accurate, compliant, and auditable.