Intricate machines and technologies now collect an incredible amount of data — over 2.5 quintillion bytes every day! — from equipment sensors, logs, users, consumers, and elsewhere. That data must be stored in a way that allows businesses to leverage it: to report on the past, to understand the present, and to predict the future. Data warehouses support reporting and analytics on historical data while data lakes support newer use cases that leverage data for machine learning, predictions, and real-time analysis.
The question is: do you need both and why?