  Applications like Yahoo, Facebook, Twitter have huge data which has to be
stored and retrieved as per client access. This huge data storage requires huge
database leading to increase in physical storage and becomes complex for
analysis required in business growth. This storage capacity can be reduced and
distributed processing of huge data can be done using Apache Hadoop which uses
Map-reduce algorithm and combines the repeating data so that entire data is
stored in reduced format. The paper describes performing a wordcount Map-Reduce
Job in Single Node Apache Hadoop cluster and compress data using
Lempel-Ziv-Oberhumer (LZO) algorithm.
