This paper describes how modern storage accelerates the data-hungry workflows behind autonomous-driving and ADAS development. It outlines ADAS data challenges—massive sensor ingestion, distributed training pipelines, strict latency requirements, and global collaboration. Tech Mahindra and Pure Storage enable faster data labeling, simulation, model iteration, and regression testing. The paper highlights high-throughput unstructured data handling, parallel processing, scalable capacity, and efficient multi-site data distribution as key enablers of faster innovation in automotive R&D.

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