TY  - JOUR
T1  - A Study on Non Java Options for Mapreduce Programming with Hadoop
AU - Ranichandra, C. AU - Tripathy, B.K. 
JO  - Asian Journal of Information Technology
VL  - 15
IS  - 16
SP  - 2999
EP  - 3003
PY  - 2016
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2016.2999.3003
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2016.2999.3003
KW  - Cloud
KW  -hadoop
KW  -mapreduce
KW  -streaming
KW  -pipes
KW  -pig
KW  -Hive
KW  -Jaql
KW  -cascading
KW  -dataflow
AB  - Storage and processing of intensively growing data has become easier with the invent of cloud computing. Computing paradigms have evolved from the era of centralized to distributed, distributed to grid and grid to cloud. Parallel programming models to process large data and with utilization of more cpus have got a new color with the introduction of mapreduce programming model. Hadoop, a framework for handling millions of data in clusters of computer uses MapReduce programming model. Users prefer java for their MapReduce jobs as Hadoop is written in Java. Here, we discuss the MapReduce programming approach and other approaches/languages that can be used to write MapReduce jobs.
ER  -