The big reason that dimensional modeling increases clarity is that the dimensional model seeks to flatten data as much as possible. Let’s compare two examples. Both of these examples are for a fictional health clinic.
The first example is that we want a report on how many male patients were treated with electric shock therapy by provider, grouped monthly and spanning year to date range.
Those big Kimball-style warehouses do a great job of making it easier for people who are not database specialists to query data and get meaningful, consistent results to known business questions. The trick to understanding data platforms is that they tend to be complements rather than substitutes: introducing Spark-R in your environment does not replace your Kimball-style warehouse; it complements it by letting analysts find trends more easily. Similarly, a Hadoop cluster potentially lets you complement an existing data warehouse in a few ways: acting as a data aggregator (which allows you to push some ETL work off onto the cluster), a data collector (especially for information which is useful but doesn’t really fit in your conformed warehouse), and a data processor (particularly for those gigantic queries which are not time-sensitive).