How to correctly select a sample from a huge dataset in machine learning

In machine learning, we often need to train a model with a very large dataset of thousands or even millions of records. The higher the size of a dataset, the higher its statistical significance and the information it carries, but we rarely ask ourselves: is such a huge dataset really useful? Or we could reach a satisfying result with a smaller, much more manageable one? Selecting a reasonably small dataset carrying the good amount of information can really make us save time and money. Let's make a simple mental experiment. Imagine that we are in a library and want to learn Dante Alighieri's Divina Commedia word by word.