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Hadoop, Big
Data and Map Reduce is very common and will be more relevant to discuss about
because most of today’s Worldwide Data is generated for approximately in just
the last recent years – and it will only continue to grow more and more in an
exponential speed. One of the biggest websites like Google or Facebook constantly
deals with massive queries with massive load of information. Google had endless
load of data and were becoming too large to process in a single database,
because it would take too long or simply weren’t enough space to process data.
So, to solve this problem, they had to come up with a better idea, a solution. The
idea was to create an algorithm to take the massive load of data and split it into
smaller unities and then map it to different computers. This simple idea became
popular among big companies today. They call this algorithm Map Reduce. Later,
this algorithm became and open source, a project called Hadoop. The idea behind
this was to have applications to be able to run with Map Reduce algorithm. Also,
Java related programming was needed to be able to run this kind of algorithm,
with only some basic knowledge of it. Therefore, we introduced Java earlier in
section 2.1.


Now, since the
clear majority today is spending a lot of time on marketing over the internet,
companies etc. this trend will only grow bigger. A cross the world; our future communication,
trades happens all over on internet and computers becoming our main source. We’ll
need better tools and software to help our businesses and companies. Amazon,
Cloudera, Mircosoft etc. are known companies that embraces Hadoop to run as an
open source software. As we can tell, Hadoop is becoming a big deal and is very
commonly used in statistical analysis. In fact, Hadoop is designed to scale up
from a single server machines to thousands of each offering local communication
and storage.

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