Hadoop is the catch word in Big data processing. Although, there are many other big data processing systems like hadoop none has become so popular and widely accepted as hadoop. Hadoop is used in very large enterprises and the list of enterprises embracing hadoop is increasing day by day (analyzing the stats from internet).
From my analysis and readings, it is clear that the real reason for the success of hadoop is its cheap storage. Hadoop doesn’t require the data to be stored on enterprise class storage systems. You can very well store the data in commodity x86 machines (cheap price tag). Coupled with this cheap storage, hadoop’s offering of redundant data storage to cater to failures in nodes in the hadoop cluster, prompted many corporations to move to cheaper storage solutions from the prohibitively expensive conventional storage systems.
Hadoop not only offers storage, but it also brings the power of map reduce in crunching big data to extract meaningful results. This cheap storage and cheap but powerful processing prompted many corporations to start storing all kinds of data and extracting meaningful actionable results out of that data. Data previously not stored or stored by not processed are now increasingly being acted upon by the enterprises thanks to Hadoop.