# Storing large data sets for python machine learning algorithm consumption

I'm reading up on how to clean/munge/wrangle data sets in order to run machine learning algorithms on them. Lots of info on how to do the actual wrangling, but a practical detail seems to be glossed over: storage.

My question is quite simple: which is the go-to technology to store/retrieve a large data set in order to run algorithms on it in the most convenient/efficient way possible?

I'm guessing the language in which the algorithms are written is not all that relevant here.

Currently Apache Hadoop is one of the popular technologies to store data and Apache Spark is a very popular computational engine to compute/munge large datasets.