Considering temporal dimensions

All of the concepts we have reviewed in this chapter become amplified by the dimension of time. For example, imagine you are ingesting and storing location-based data for marathon runners in the United States. Let’s say this number is around 500,000. Creating a heat map to show concentrations of where marathon runners live already means dealing with a reasonably large dataset. Now, consider the locations of each race that the runners participated in over the course of a year. You get even better location insights into the marathon activity over the year, but you are likely dealing with millions of data points. The data grows exponentially when you start to look at GPS data from fitness trackers along the marathon routes. There are over 1,000 marathons each year in the US, and the largest marathons have over 10,000 finishers. Multiply that by the GPS location of each runner, and even at 10-second intervals, the data volumes become enormous. Conventional...