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Data pipelines play a critical role in the performance of large-scale pre-training jobs running on thousands of GPUs. In this work, we present a comprehensive quantitative analysis of data access patterns from production workloads and reveal three previously underreported bottlenecks. First, cross-datacenter traffic emerges as a major source of latency when evaluating in-training models using remote checkpoints. Second, checkpoint loading during startup phases frequently suffers from I/O contention that delays job initialization. Third, data transformation during loading becomes a significant and CPU-intensive bottleneck for multimodal models. Guided by these findings, we introduce three optimizations: global-namespace-based predictive checkpoint replication, proactive hot-file replication, and offloading data transformation to storage-tier CPU resources. Crucially, we demonstrate that these optimizations are not system-specific but address fundamental architectural mismatches in the LLM era. They are broadly applicable to both legacy and modern storage systems, offering a high-return path to upgrade infrastructure with minimal engineering intrusion. Together, these techniques reduce wasted GPU hours per evaluation from 16,800 to 4,000, shorten checkpoint loading time at each training start by 40.8%, and reduce training stalls caused by dataloading by 63.2%.