R and Big Data
Philippe De Brouwer · 2020
The academic definition of big data implies that the data has to be big in terms of velocity, variety, veracity, and volume. We consider our data to be “big” if it is no longer practically possible to store all data on one machine and/or use all processing units of that one machine to do all calculations (such as calculating a mean or fitting a neural network). When a personal computer fails to get the task done, it might be possible for a larger computer to handle the task. Depending on the exact problem, it might be necessary to install more RAM, more cores, more disk space or a combination of all of the above. It is also possible to use R as a service that runs on a server.