It's not a naive question, it's an important question. Sampling is almost always the first thing I think of when I have a "big" dataset in front of me, and it r

It's not a naive question, it's an important question. Sampling is almost always the first thing I think of when I have a "big" dataset in front of me, and it requires some careful thought. It really depends on what you want to do. If you want to compute a statistic over a large amount of data or build a predictive model, some sort of sampling may create virtually identical results with orders of magnitude less time/memory. In modeling, sampling away certain classes may actually be essential for algorithmic stability. Sometimes sampling is wrong. If you have to create statistics over your entire dataset (let's say, number of transactions aggregated by user), sampling may not be worth the loss of precision, and it won't really save you much time either. In general, you never want to sample away data in a way that the confidence in the statistic you are calculating becomes low.