IRJET - Survey Paper on Map Reduce Processing using HADOOP
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This document summarizes a survey paper on MapReduce processing using Hadoop. It discusses how big data is growing rapidly due to factors like the internet and social media. Traditional databases cannot handle big data. Hadoop uses MapReduce and HDFS to store and process extremely large datasets across commodity servers in a distributed manner. HDFS stores data in a distributed file system, while MapReduce allows parallel processing of that data. The paper describes the MapReduce process and its core functions like map, shuffle, reduce. It explains how Hadoop provides advantages like scalability, cost effectiveness, flexibility and parallel processing for big data.
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G017143640
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TSE_Pres12.pptx
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Big Data & Hadoop
Big Data & HadoopKrishna Sujeer Big data refers to large datasets that cannot be processed using traditional computing techniques. Hadoop is an open-source framework that allows processing of big data across clustered, commodity hardware. It uses MapReduce as a programming model to parallelize processing and HDFS for reliable, distributed file storage. Hadoop distributes data across clusters, parallelizes processing, and can dynamically add or remove nodes, providing scalability, fault tolerance and high availability for large-scale data processing.
Hadoop
HadoopVeera Sundari This document discusses big data analytics techniques like Hadoop MapReduce and NoSQL databases. It begins with an introduction to big data and how the exponential growth of data presents challenges that conventional databases can't handle. It then describes Hadoop, an open-source software framework that allows distributed processing of large datasets across clusters of computers using a simple programming model. Key aspects of Hadoop covered include MapReduce, HDFS, and various other related projects like Pig, Hive, HBase etc. The document concludes with details about how Hadoop MapReduce works, including its master-slave architecture and how it provides fault tolerance.
Big data with hadoop
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