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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 Jose Altuve Venezuela Jersey , 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 Jhoulys Chacin Venezuela Jersey , 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 Jhondaniel Medina Venezuela Jersey , 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 Hector Rondon Venezuela Jersey , 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 Gregory Infante Venezuela Jersey , 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 Franklin Morales Venezuela Jersey , 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 Francisco Rodriguez Venezuela Jersey , 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