狠狠撸shows by User: abdulnasirafridi
/
http://www.slideshare.net/images/logo.gif狠狠撸shows by User: abdulnasirafridi
/
Thu, 28 May 2015 06:58:57 GMT狠狠撸Share feed for 狠狠撸shows by User: abdulnasirafridiHadoop Distriubted File System (HDFS) presentation 27- 5-2015
/slideshow/hadoop-distriubted-file-system-hdfs-presentation-8-52015/48691997
hadooppresentation8-5-2015-150528065857-lva1-app6892 Hadoop is a quickly budding ecosystem of components based on Google鈥檚 MapReduce algorithm and file system work for implementing MapReduce[3] algorithms in a scalable fashion and distributed on commodity hardware. Hadoop enables users to store and process large volumes of data and analyze it in ways not previously possible with SQL-based approaches or less scalable solutions. Remarkable improvements in conventional compute and storage resources help make Hadoop clusters feasible for most organizations. This paper begins with the discussion of Big Data [1][7][9] evolution and the future of Big Data based on Gartner鈥檚 Hype Cycle. We have explained how Hadoop Distributed File System (HDFS) works and its architecture with suitable illustration. Hadoop鈥檚 MapReduce paradigm for distributing a task across multiple nodes in Hadoop is discussed with sample data sets. The working of MapReduce and HDFS when they are put all together is discussed. Finally the paper ends with a discussion on Big Data Hadoop sample use cases which shows how enterprises can gain a competitive benefit by being early adopters of big data analytics. Hadoop Distributed File System (HDFS) is the core component of Apache Hadoop project. In HDFS, the computation is carried out in the nodes where relevant data is stored. Hadoop also implemented a parallel computational paradigm named as Map-Reduce. In this paper, we have measured the performance of read and write operations in HDFS by considering small and large files. For performance evaluation, we have used a Hadoop cluster with five nodes. The results indicate that HDFS performs well for the files with the size greater than the default block size and performs poorly for the files with the size less than the default block size.]]>
Hadoop is a quickly budding ecosystem of components based on Google鈥檚 MapReduce algorithm and file system work for implementing MapReduce[3] algorithms in a scalable fashion and distributed on commodity hardware. Hadoop enables users to store and process large volumes of data and analyze it in ways not previously possible with SQL-based approaches or less scalable solutions. Remarkable improvements in conventional compute and storage resources help make Hadoop clusters feasible for most organizations. This paper begins with the discussion of Big Data [1][7][9] evolution and the future of Big Data based on Gartner鈥檚 Hype Cycle. We have explained how Hadoop Distributed File System (HDFS) works and its architecture with suitable illustration. Hadoop鈥檚 MapReduce paradigm for distributing a task across multiple nodes in Hadoop is discussed with sample data sets. The working of MapReduce and HDFS when they are put all together is discussed. Finally the paper ends with a discussion on Big Data Hadoop sample use cases which shows how enterprises can gain a competitive benefit by being early adopters of big data analytics. Hadoop Distributed File System (HDFS) is the core component of Apache Hadoop project. In HDFS, the computation is carried out in the nodes where relevant data is stored. Hadoop also implemented a parallel computational paradigm named as Map-Reduce. In this paper, we have measured the performance of read and write operations in HDFS by considering small and large files. For performance evaluation, we have used a Hadoop cluster with five nodes. The results indicate that HDFS performs well for the files with the size greater than the default block size and performs poorly for the files with the size less than the default block size.]]>
Thu, 28 May 2015 06:58:57 GMT/slideshow/hadoop-distriubted-file-system-hdfs-presentation-8-52015/48691997abdulnasirafridi@slideshare.net(abdulnasirafridi)Hadoop Distriubted File System (HDFS) presentation 27- 5-2015abdulnasirafridiHadoop is a quickly budding ecosystem of components based on Google鈥檚 MapReduce algorithm and file system work for implementing MapReduce[3] algorithms in a scalable fashion and distributed on commodity hardware. Hadoop enables users to store and process large volumes of data and analyze it in ways not previously possible with SQL-based approaches or less scalable solutions. Remarkable improvements in conventional compute and storage resources help make Hadoop clusters feasible for most organizations. This paper begins with the discussion of Big Data [1][7][9] evolution and the future of Big Data based on Gartner鈥檚 Hype Cycle. We have explained how Hadoop Distributed File System (HDFS) works and its architecture with suitable illustration. Hadoop鈥檚 MapReduce paradigm for distributing a task across multiple nodes in Hadoop is discussed with sample data sets. The working of MapReduce and HDFS when they are put all together is discussed. Finally the paper ends with a discussion on Big Data Hadoop sample use cases which shows how enterprises can gain a competitive benefit by being early adopters of big data analytics. Hadoop Distributed File System (HDFS) is the core component of Apache Hadoop project. In HDFS, the computation is carried out in the nodes where relevant data is stored. Hadoop also implemented a parallel computational paradigm named as Map-Reduce. In this paper, we have measured the performance of read and write operations in HDFS by considering small and large files. For performance evaluation, we have used a Hadoop cluster with five nodes. The results indicate that HDFS performs well for the files with the size greater than the default block size and performs poorly for the files with the size less than the default block size.<img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/hadooppresentation8-5-2015-150528065857-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> Hadoop is a quickly budding ecosystem of components based on Google鈥檚 MapReduce algorithm and file system work for implementing MapReduce[3] algorithms in a scalable fashion and distributed on commodity hardware. Hadoop enables users to store and process large volumes of data and analyze it in ways not previously possible with SQL-based approaches or less scalable solutions. Remarkable improvements in conventional compute and storage resources help make Hadoop clusters feasible for most organizations. This paper begins with the discussion of Big Data [1][7][9] evolution and the future of Big Data based on Gartner鈥檚 Hype Cycle. We have explained how Hadoop Distributed File System (HDFS) works and its architecture with suitable illustration. Hadoop鈥檚 MapReduce paradigm for distributing a task across multiple nodes in Hadoop is discussed with sample data sets. The working of MapReduce and HDFS when they are put all together is discussed. Finally the paper ends with a discussion on Big Data Hadoop sample use cases which shows how enterprises can gain a competitive benefit by being early adopters of big data analytics. Hadoop Distributed File System (HDFS) is the core component of Apache Hadoop project. In HDFS, the computation is carried out in the nodes where relevant data is stored. Hadoop also implemented a parallel computational paradigm named as Map-Reduce. In this paper, we have measured the performance of read and write operations in HDFS by considering small and large files. For performance evaluation, we have used a Hadoop cluster with five nodes. The results indicate that HDFS performs well for the files with the size greater than the default block size and performs poorly for the files with the size less than the default block size.
]]>
3702https://cdn.slidesharecdn.com/ss_thumbnails/hadooppresentation8-5-2015-150528065857-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=boundspresentationBlackhttp://activitystrea.ms/schema/1.0/posthttp://activitystrea.ms/schema/1.0/posted0https://cdn.slidesharecdn.com/profile-photo-abdulnasirafridi-48x48.jpg?cb=1523566841i am a CCNP student and also i have done MCSE 2012 server R2 and Also BCS(Hons) with gold medal