Naive data iteration—which loads an entire dataset into memory and performs random memory access—is inefficient and can cause memory issues for real-world problems. Built-in iterators implemented in deep learning frameworks are considerably more efficient. They are designed to seamlessly handle large data sources, such as data stored in files, received via streams, or generated on the fly, without requiring all data to be loaded into memory simultaneously. Furthermore, these well-implemented data iterators exploit high-performance computing, such as utilizing GPUs for rapid image decompression, video transcoding, and other preprocessing tasks, to prevent data input/output from slowing down the overall training loop.