Data lakes are setup as large physical storage repositories. Data from all kinds of sources is copied into this repository, which is commonly developed with Hadoop. But is this centralized storage approach really feasible and practical? Additionally, the users of data lakes are data scientists and other investigative users.

Not every organization is happy with the reporting and analytical performance of their data warehouse environment. One customer indicated that some of their online reports take at least ten minutes to complete. Ten minutes is a long time if you have to wait for a report to show up on your screen.

The classic data warehouse architecture, consisting of a chain databases and ETL processes, has served may organizations well the last twenty-five years; see Figure 1. But is it the still the right architecture?

Which organization doesn’t want a perfect 360-degree view of their customers? One click on a button, and everything that’s known about a customer is shown. Every organization wants this. But how? In this second article in a series on use cases of data virtualization we describe how this technology can help create this perfect view.