Comparison between RDBMS and Map-Reduce

	RDBMS	Map-Reduce
Scalability	Scale UP	Scale Out
Data Size	GB	PB and more
Read/Write	Batch and Interactive	Batch
Update Type	Write many Read many	Write once Read many
Integrity	High	Low
Structure	Structural data/ Schema first Write later	Non Structural data/ Write first Schema later
Query	SQL	No SQL and SQL support too with add on tools
Response time	Faster for Less data/ slow once size increases	Faster for more data in comparison
Note: Slowly with the new sub projects being developed for Hadoop this gap is being filled up as people are developing an abstract layer on top of Map-Reduce and YARN frameworks which takes SQL and in turn convert it to Map-Reduce/Yarn.

Apache Hadoop NextGen MapReduce (YARN)

MapReduce has undergone a complete overhaul in hadoop-0.23 and we now have, what we call, MapReduce 2.0 (MRv2) or YARN.

The fundamental idea of MRv2 is to split up the two major functionalities of the JobTracker, resource management and job scheduling/monitoring, into separate daemons. The idea is to have a global ResourceManager (RM) and per-application ApplicationMaster (AM). An application is either a single job in the classical sense of Map-Reduce jobs or a DAG of jobs.

The ResourceManager and per-node slave, the NodeManager (NM), form the data-computation framework. The ResourceManager is the ultimate authority that arbitrates resources among all the applications in the system.

The per-application ApplicationMaster is, in effect, a framework specific library and is tasked with negotiating resources from the ResourceManager and working with the NodeManager(s) to execute and monitor the tasks.

 

Check this LINK for more detail