Hadoop
Certification Training Institute in Noida :- Hadoop is an open-source framework
that allows to store and process big data in a distributed environment across clusters
of computers using simple programming models. It is designed to scale up from
single servers to thousands of machines, each offering local computation and
storage. This brief tutorial provides a quick introduction to Big Data, Map
Reduce algorithm, and Hadoop Distributed File System. This tutorial has been
prepared for professionals aspiring to learn the basics of Big Data Analytics
using Hadoop Framework and become a Hadoop Developer. Software Professionals,
Analytics Professionals, and ETL developers are the key beneficiaries of this
course.
Due to the advent of new technologies, devices, and
communication means like social networking sites, the amount of data produced
by mankind is growing rapidly every year. The amount of data produced by us from
the beginning of time till 2003 was 5 billion gigabytes. If you pile up the
data in the form of disks it may fill an entire football field. The same amount
was created in every two days in 2011, and in every ten minutes in 2013. This
rate is still growing enormously. Though all this informat. Big data
technologies are important in providing more accurate analysis, which may lead
to more concrete decision-making resulting in greater operational efficiencies,
cost reductions, and reduced risks for the business. Hadoop
Certification Training in Noida
To harness the power of big data, you would require an
infrastructure that can manage and process huge volumes of structured and unstructured
data in real-time and can protect data privacy and security. There are various
technologies in the market from different vendors including Amazon, IBM,
Microsoft, etc., to handle big data. While looking into the technologies that
handle big data, we examine the following two classes of technology. Google
solved this problem using an algorithm called Map Reduce. This algorithm
divides the task into small parts and assigns them to many computers, and
collects the results from them which when integrated, form the result dataset.
Using the solution provided by Google, Doug Cutting and his
team developed an Open Source Project called HADOOP. Hadoop runs applications
using the Map Reduce algorithm, where the data is processed in parallel with
others. In short, Hadoop is used to develop applications that could perform
complete statistical analysis on huge amounts of data.
The Hadoop Distributed File System (HDFS) is based on the
Google File System (GFS) and provides a distributed file system that is designed
to run on commodity hardware. It has many similarities with existing
distributed file systems. However, the differences from other distributed file
systems are significant. It is highly fault-tolerant and is designed to be
deployed on low-cost hardware. It provides high throughput access to
application data and is suitable for applications having large datasets.
It is quite expensive to build bigger servers with heavy
configurations that handle large scale processing, but as an alternative, you
can tie together many commodity computers with single-CPU, as a single
functional distributed system and practically, the clustered machines can read
the dataset in parallel and provide a much higher throughput. Moreover, it is
cheaper than one high-end server. So this is the first motivational factor
behind using Hadoop that it runs across clustered and low-cost machines.
No comments:
Post a Comment