The power of Hadoop is that it utilises schema on read. With a data warehouse, you often have to know what the tables look like before loading data. With Hadoop, you can pull data from any source or type and then figure out how to organize it. Organisations are beginning to use Hadoop as a dumping ground for all kinds of data because it is inexpensive and doesn’t require a schema on write. Such storage is often referred to as a Hadoop “data lake.” On the flip side, the Hadoop/MapReduce engine is not optimised for the iterative processing that analytics often requires. It is best suited to batch processing.