Although the most commonly encountered big data sets right now involve images and videos, big datasets occur in many other domains and involve many other kinds of data types: web pages, financial transactions, network traces, brain scans, etc.
However, working with the large amount of data sets presents a number of challenges:

Dataset Size: datasets often exceed the capacity of node-local disk storage, requiring distributed storage systems and efficient network access.
Number of Files: datasets often consist of billions of files with uniformly random access patterns, something that often overwhelms both local and network file systems.
Data Rates: training jobs on large datasets often use many GPUs, requiring aggregate I/O bandwidths to the dataset of many GBytes/s; these can only be satisfied by massively parallel I/O systems.
Shuffling and Augmentation: training data needs to be shuffled and augmented prior to training.
