Unifying File and Object Storage in AI Infrastructure

This white paper discusses the architectural transition enterprises face due to the rapid growth of unstructured data and the demands of AI and machine learning workloads. It outlines the challenges of managing both file and object storage systems, which have traditionally operated in isolation, leading to inefficiencies and performance bottlenecks. The document analyzes the semantic gaps between these two storage paradigms and evaluates the limitations of first-generation unification solutions. It introduces a third approach known as Unified Data Fabric, which aims to bridge the divide between file and object storage without compromising performance or data sovereignty. By leveraging advanced protocols and direct object-to-file mapping, this architecture seeks to eliminate translation bottlenecks and enhance data-centric workflows across industries. The paper emphasizes the need for a converged architecture to support the increasing demands of AI initiatives and ensure organizations can effectively manage their growing data assets.