  Hadoop is a distributed batch processing infrastructure which is currently
being used for big data management. The foundation of Hadoop consists of Hadoop
Distributed File System or HDFS. HDFS presents a client server architecture
comprised of a NameNode and many DataNodes. The NameNode stores the metadata
for the DataNodes and DataNode stores application data. The NameNode holds file
system metadata in memory, and thus the limit to the number of files in a file
system is governed by the amount of memory on the NameNode. Thus when the
memory on NameNode is full there is no further chance of increasing the cluster
capacity. In this paper we have used the concept of cache memory for handling
the issue of NameNode scalability. The focus of this paper is to highlight our
approach that tries to enhance the current architecture and ensure that
NameNode does not reach its threshold value soon.
