Why do TensorFlow pipelines break when dataset size increases significantly?

Pipelines break due to memory overflow, IO bottlenecks, and insufficient parallelism.

When datasets scale, naive pipelines load too much data into memory or fail to parallelize transformations. Disk IO becomes a bottleneck, and CPU preprocessing cannot keep up with GPU consumption. Without tf.data optimizations like caching, prefetching, and parallel mapping, performance collapses.