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As blending both GIS and cloud computing
opens a new era for the advancement of data storing, processing and its
application for the GIS 3, 4.The
cloud-computing based open source platforms represent the ground up development
to solve the limitations and restrictions found in traditional GIS. The
cloud-computing based open source platforms are the most transparent platforms for
building GIS cloud.

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Recently, Hadoop 18, 19 is
the most well known open source cloud-computing platform since its release in
2007. Hadoop was written in Java and funded by Apache. It is implemented for Google MapReduce as an open source.Hadoop represents a solution for scalable processing of huge datasets in many applications. It has been
intended to dodge the low execution and the complexity experienced when
processing and analysing large data utilizing traditional technologies. One
fundamental advantage of Hadoop is its ability to swiftly process large data
sets, because of its parallel clusters and distributed file system. Actually,
dissimilar to traditional technologies, Hadoop does not duplicate in memory the
entire remote data to complete computations. Preferably, Hadoop executes
processes where data exist. In this way, Hadoop reduces the considerable
network and servers communication load. Hadoop is not schema oriented so it can
retain any kind of data, structured or not, from various sources. Data from
various sources can be joined and processed in arbitrary ways empowering
further analysis than any other system can give. Also, when a node was lost,
Hadoop diverts work to another location of the data and keeps processing20.additionally, when required new nodes can be included without
changing the data formats, the way which data is loaded, the way the tasks or
applications are composed.The Hadoop platform power is based on two main subcomponents: the Hadoop
Distributed File System (HDFS) for storage and the MapReduce framework for

Distributed File System (HDFS)

HDFS is one of the essential components of Hadoop cluster. HDFS is a
Java file system, developed utilizing distributed file framework design that
implements scalable and reliable data storage. It was developed to spread over
clusters of servers. Dissimilar to other distributed frameworks, HDFS is
greatly fault-tolerant and built utilizing ease hardware. HDFS manipulates a
massive amount of data and supports easier access. To store such huge data, the
files are stored on various servers in a duplicated fashion to protect the
system from potential data losses in case of failure. HDFS additionally makes
applications accessible for parallel processing. HDFS has exhibited high
composition scalability.

Generally, HDFS was developed as Master-Slave model. In which, the user
data is stored in files of HDFS. The file in HDFS is fragmented into a set of
fragments and/or stored in singular data nodes. These file fragments are called
blocks. In other words, the base unit of data that HDFS can read or write is
called a Block.

in HDFS, NameNode represents the master server in the Hadoop cluster
that deals with the file system namespace tasks like opening, closing, renaming
files and directories. Furthermore, NameNode decides the mapping of blocks to
DataNodes alongside directing access to files by clients.

DataNodes represent the slave nodes in the Hadoop cluster. DataNodes are
in charge of serving read and write processes on the file systems, according to
the client demand alongside performing processes such as block creation,
deletion, and replication upon instruction from the Master node (NameNode).

At the point when a
client makes a demand of a Hadoop cluster, the JobTracker maintains this
demand. The JobTracker, working with the NameNode, disseminates work as nearly
as feasible to the data on which it will work. The NameNode on the master
server of the file system, supplying metadata services for data distribution
and replication. The JobTracker registers map and reduce tasks into accessible
slots at one or more TaskTrackers.

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