A
corporate data stores continue to grow almost 50% annually, and this increase in
data storage requires a good management and this indirectly needs a change in
the current technology. Storage and management technology has evolved to an
extent but still today’s enterprises are faced with many evolving needs that
can strain storage technologies. However, the big data analytics process
demands the capabilities that are beyond the typical storage that should handle
terabytes and petabytes of unstructured information which is big challenge. So it
is requiring something more, a new way or a platform to deal with large volumes
of data.
Hadoop:-
Hadoop is an open-source
software framework that offers a platform to deal with big data. Hadoop was
derived from Google’s MapReduce and Google File System papers. Hadoop is
written in Java programming language and is an Apache top-level project being
built.
Hadoop platform is
designed to solve problems caused by large amounts of data that contain complex
and unstructured data which cannot be placed into the tables directly. Hadoop solves
the most common problem with the big data i.e. efficiently storing and
accessing large amounts of data.
The intrinsic design of
Hadoop allows it to run as a platform that is able to work across a large
number of machines that don’t share any memory or disks. It reduces the
management overhead associated with the large data sets. The Hadoop framework
provides both reliability and data motion to applications. The data is being
loaded into Hadoop platform, the software breaks down the data into fragments
which are then spread across different servers. The distributed nature of the
data means there is no more traditional data centered server where the data has
been stored and has to go to access the data. Furthermore, Hadoop keeps tracks
of the data where it resides and it also protects multiple copies of the
information.
Unlike the limitations
associated with the centralized database system, which may consist of a large
disk drive connected to a server that features multiple processors, with Hadoop
every sever in the cluster is allowed to participate in the processing of the
data through Hadoop’s capability to spread the work and the data across the
cluster and each server then operates it’s little piece of data and then all
the results is unified.
Hadoop is referred to as
MapReduce where the code and processes are mapped to all servers and the
results are reduced into a single set. This process makes Hadoop to deal with
massive data.
However, there are certain
pre-requisites, hardware requirements and configuration that must be met to
ensure success. Big data analytics requires that organizations should choose
the data to analyze it and then apply aggregate methods before it goes for
extract, transform and load process. The data can be structured, unstructured or
from multiple sources such as social networks, data logs, websites etc which is
accomplish by processes and considerations such as the capability to move
computing power closer to the data and perform parallel or batch processing of
large data sets.
But Hadoop cannot
accomplish everything on it’s own. Organizations will need to consider what
additional components are required to build a Hadoop project. For effective
management and implementation of Hadoop require some expertise and experiences,
and if it is not available then it should take help of the service provider that
can offer full support for the Hadoop project.
Although Hadoop has been
around for some time, more and more organizations had started using its
capabilities and had reduced the challenge of handling the big data processing.
It also includes the major companies who are associated with Hadoop such as
Google, Yahoo, JP Morgan Chase etc.
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