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The cloud is quickly becoming the engine room for data generation, management, and storage, reshaping how infrastructure must evolve to support next-generation compute. Neoclouds are rapidly emerging to meet the demands of model training, inference, and massive parallel workloads, while traditional architectures strain under exponential data growth. These shifts are redefining how data pipelines are built, how storage is structured, and how organizations balance cost, performance, and sustainability across hybrid environments while the choice between cloud and on-prem becomes increasingly strategic as the scale and velocity of workloads expand. Join this episode to discover:

• How cloud-based data generation and storage are adapting to next-generation compute
• The impact of data growth on pipeline architecture and model performance
• How training and inference in the cloud reshape data processing requirements
• How to balance cost, sustainability, and placement across cloud and on-prem environments