Benefits and Significance of Hadoop in Big Data

Hadoop, which is as of now widely used in big data applications in the organizations, for the purpose of spam filtering, network searching, clickstream analysis, and social recommendation.

Apart from just that considerable academic research is now based on the Big Data tool that is Hadoop. Some representative cases are given below.

One of the largest social media channels, Facebook announced that their Hadoop cluster can process 100 PB data, which has grown by 0.5 PB per day by the end of the year 2012. Apart from just that, many companies provide Hadoop commercial execution and/or support, some of them include the Cloudera, IBM, MapR, EMC, and Oracle.

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Among modern industrial machinery and systems, sensors are widely deployed to collect information for failure forecasting and environmental monitoring, etc. Gunarathne, utilized cloud computing infrastructures, Amazon AWS, Microsoft Azure, and data processing framework which is based on MapReduce,

Hadoop, and Microsoft DryadLINQ to run two parallel bio-medicine applications: (i) assembly of genome segments; (ii) and secondly in the dimension reduction in the analysis of chemical structure. In the subsequent application, the 166-D datasets used include the data points of 26,000,000.

The authors compared the performance of all the frameworks in terms of efficiency, availability, cost.

According to the study which has been revealed, the authors concluded that the loose coupling will be increasingly applied to research on electron cloud, and the parallel programming technology (MapReduce) framework may provide the user interface with some of the more convenient services and reduce unnecessary costs.

Data Mining:

As there is the unstoppable growth of the internet in today’s world, the overall data (online and offline) is increasing in each and every domain. There was the time when standalone data mining tools were enough to do the task of data mining but increased data volumes demands distributed approach in order to save money, time, and energy. The most popular distributed processing framework Hadoop which is also being one of the major latest technological innovation in the IT world.

As more data centers support the Hadoop framework, as in today world it becomes very important to migrating existing data mining approaches or algorithms onto the Hadoop platform for increased parallel processing efficiency.

In this world of the modern era, where organizations are highly rich in data, the true value lies in the ability to collect this data, sort and analyze it such that it yields the actionable business intelligence.

To analyze the data, some of the more algorithms which are based on traditional data mining like clustering, classification form the basis for machine learning processes in the business intelligence support tools.

As the IT industries began with the help of larger volumes of data, migrating it over the network for the purpose of transformation or analysis has nowadays also become unrealistic.

Moving terabytes of data from one system to another daily can be a huge task to the network administrator down on a program which does not make more to push the processing to the data.

Moving all the big data to one storage area network or the ETL server has become one of the most feasible with big amounts of data. Even if you can move the data, processing is slow and limited to SAN bandwidth, and often fails to meet such that batch processing windows.

A Java-based programming framework Hadoop which supports the processing of large volumes of data sets in a much more distributed computing environment and is part of the Apache project which is highly sponsored by the Apache Software Foundation.

Hadoop, which was as of now originally conceived on the basis of Google’s MapReduce, in which an application is broken down into little small pieces. The Apache Hadoop software library can also be used to detect and handle failures at all the major application layer and can deliver a highly-available service on top of a cluster of computers, each of which may be even prone to failures.

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Hadoop can provide much-needed scalability and robustness option to a large set of a distributed system as Hadoop provides inexpensive and reliable storage.

The Hadoop framework is the software framework, which is used for writing applications that process vast amounts of data in parallel on large clusters of compute nodes at a very faster pace and it works on MapReduce programming model which is a high and large generic execution engine that parallelizes computation over a large cluster of machines.

MapReduce is more just like a distributed Programming Model which is much more used and intended for a large cluster of systems that can work in parallel on a large set of data.

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The Jobtracker is responsible for handling both the reduce and map process. The tasks which are evenly divided by the main application are firstly processed by the map tasks in a completely parallel manner. The MapReduce framework sorts the outputs of the maps, which are given by them as an input to the reduce tasks.

Both the input and output of the job are stored in the system which is protected. Because of the parallel computing nature of MapReduce, parallelizing data mining algorithms work on the principle of the MapReduce model, which has become popular and has received significant attention from the some of the high research community.

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