The Hadoop is an open source
system used to store, process and analyze huge amounts of data thousands of
terabytes, petabytes or even huge amounts of data.
This system emerges as an
initiative of free software (open source) through the generation of various
Google papers on file systems, the mapping tool in addition to the BigTable
Reduce system. From this result it has emerged from a series of solutions in
the Apache environment.The success of it is basically economic, by providing
value to companies in terms of information storage.
It presents an easy and
convenient way to manage disparate data and make sense so that we can obtain
useful information to improve productivity and business growth. The best way to
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How is Hadoop designed?
The processing of big data
together, usually requires several high performance teams also, the
installation of specialized high-cost hardware, with which a joint processing
of big data can be done.
It is basically designed to scale
from a single server to thousands of machines, offering calculation and storage
capacity; also serves to detect and control errors in terms of applications to
have more reliability.
It has an architecture that
facilitates the efficient analysis of large amounts of unstructured data adding value to the decision-making process in
the area that makes up the strategy area.
Likewise, it improves all
production processes, optimizing costs, by carrying out a constant monitoring
and control process. It also highlights the versatility and ability to ensure
availability for data recovery.
These characteristics of the Hadooparchitecture adapt perfectly to the needs of the Big Data universe ; both for
its storage and to allow the exchange of files.
Some studies indicate that up to
80% of the data with which companies work today they are not perfectly adequate
by rows and columns. On the contrary, it is a mess of emails, GPS signals,
satellite images, social media sources; in addition to the recognition of
servers and other files that are located unstructured.
Main Components:
The Hadoop project, in its stable version (to 1.0),
currently under the tutelage of the Apache Foundation, includes the following
modules, maintained as subprojects
Hadoop Common: contains
a set of utilities and the underlying framework that supports the other Hadoop
subprojects. Used throughout the application, it has several libraries,
such as those used for data serialization and file manipulation. It is
also in this subproject that the interfaces are made available to other file
systems, such as Amazon S3 and CloudSource
Hadoop MapReduce: implements
a programming model in the form of a class library specialized in the
processing of distributed data sets in a computational cluster. It
abstracts all parallel computing in only two
functions: Map and Reduce
Hadoop Distributed
File System (HDFS): a native Hadoop distributed file system. It
allows the storage and transmission of large data sets on low cost
machines. It has mechanisms that characterize it as a highly fault
tolerant system.
Benefits of using Hadoop
Another advantage of it is the
ease it has to skillfully manage any type of file or format so that companies
can develop plans and projects that were initially impossible to carry out.
Averaging a profit close to
fifty-two billion dollars for that period. The increase in demand for the
BigData is the engine on which the development of the market is based.
Currently, this predominates in the service sector, with a fifty percent share
of the world market.
This is how the it’s services
market is divided into several strategic areas such as: consultancy, training
and integration as well as deployment and support services.