Apache Hadoop is one of the most widely used open-source tools for making sense of Big Data. In today’s digitally driven world, every organization needs to make sense of data on an ongoing basis. Hadoop is an entire ecosystem of Big Data tools and technologies, which is increasingly being deployed for storing and parsing of Big Data.
Definition of Apache Hadoop
It is an open-source
data platform or framework developed in Java, dedicated to store and analyze
large sets of unstructured data.
With the data
exploding from digital media, the world is getting flooded with cutting-edge
Big Data technologies. However, Apache Hadoop was the first one that reflected
this wave of innovation. Let us find out what Hadoop software is and its
ecosystem. In this blog, we will learn about the entire Hadoop ecosystem that
includes Hadoop applications, Hadoop Common, and Hadoop framework.
How did Apache Hadoop evolve?
Inspired by Google’s
MapReduce, which splits an application into small fractions to run on different
nodes, scientists Doug Cutting and Mike Cafarella created a platform called Hadoop 1.0 and launched it in the year 2006 to
support the distribution of Nutch
search engine.
Apache Hadoop was made
available for the public in November 2012 by Apache Software Foundation. Named after a yellow soft-toy elephant of Doug Cutting’s kid,
this technology has been continuously revised since its launch.
As part of its
revision, Apache Software Foundation launched its second revised version Hadoop 2.3.0 on February 20, 2014, with some major changes in the
architecture.
What is Hadoop streaming?
Hadoop streaming is
the generic API that is used for working with streaming data. Both the Mapper
and the Reducer obtain their inputs in a standard format. The input is taken
from Stdin and the output is sent to Stdout. This is the method within Hadoop
for processing a continuous stream of data.
Hadoop is an application that is used for Big Data processing and storing. Hadoop
development is the task of computing Big Data through the use of various
programming languages such as Java,
Scala, and others.
Hadoop supports a range of data types such as Boolean, char, array, decimal, string, float, double, and so on.
Why should we use Apache
Hadoop?
With evolving big data
around the world, the demand for Hadoop Developers is increasing at a rapid
pace. Well-versed Hadoop Developers with the knowledge of practical
implementation are very much required to add value into the existing process.
However, apart from many other reasons, the following are the main reasons to use
this technology:
·
Extensive use of Big Data: More and more companies are realizing that in
order to cope with the outburst of data, they will have to implement a
technology that could subsume such data into itself and come out with something
meaningful and valuable. Hadoop has certainly addressed this concern, and
companies are tending toward adopting this technology. Moreover, a survey
conducted by Tableau reports that among 2,200 customers,
about 76 percent of them who are already using Hadoop wish to use it in newer
ways.
·
Customers expect security: Nowadays, security has become one of the major
aspects of IT infrastructure. Hence, companies are keenly investing in the
security elements more than anything. Apache Sentry, for instance, enables
role-based authorization to the data stored in the Big Data cluster.
·
Latest technologies taking charge: The trend of Big Data is going upward as users
are demanding higher speed and thus are rejecting the old school data warehouses.
Realizing the concern of its customers, Hadoop is actively integrating the latest
technologies such as Cloudera Impala, AtScale, Actian Vector, Jethro, etc. in
its basic infrastructure.
Why do we need Hadoop?
The explosion of big
data has forced companies to use the technologies that could help them manage
the complex and unstructured data in such a way that maximum information could
be extracted and analyzed without any loss and delay. This necessity sprouted
the development of Big Data technologies that are able to process multiple
operations at once without a failure.
Some of the features of Hadoop are as listed
below:
·
Capable
of storing and processing complex datasets: With increasing volumes of data, there is a greater possibility of data loss and failure. However, Hadoop’s ability to store and
process large and complex unstructured datasets makes it somewhat special.
·
Great
computational ability: Its the distributed computational model enables fast processing of big data with
multiple nodes running in parallel.
·
Lesser
faults: Implementing it
leads to a lesser number of failures as the jobs are automatically redirected to
other nodes as and when one node fails. This ultimately causes the system to
respond in real-time without failures.
·
No
preprocessing required: Enormous
data can be stored and retrieved at once, including both structured and
unstructured data, without having to preprocess them before storing into the
database.
·
Highly
scalable: It is a highly
scalable Big Data tool as you can raise the size of the cluster from a single
machine to thousands of servers without having to administer extensively.
·
Cost-effective: Open-source technologies come free of
cost and hence require a lesser amount of money for implementing them.
