PPT-Data Processing with MapReduce

Yasin N Silva and Jason Reed Arizona State University 1 This work is licensed under a Creative Commons Attribution NonCommercial ShareAlike 40 International License See httpcreativecommonsorglicensesbyncsa40 for details. : Simplified Data Processing on Large Clusters. Jeffrey Dean & . Sanjay . Ghemawat. Appeared in:. OSDI '04: Sixth Symposium on Operating System Design and Implementation, San Francisco, CA, December, 2004. . S. A. L. S. A. . HPC Group . http://. salsahpc.indiana.edu. School of Informatics and Computing. Indiana University. Judy . Qiu. . Thilina. . Gunarathne. . CAREER Award. Outline. Iterative . and . Hadoop. Debapriyo Majumdar. Data Mining – Fall 2014. Indian Statistical Institute Kolkata. November 10, 2014. Let’s keep the intro short. Modern data mining: process immense amount of data quickly. , Collective Communication, and Services. Oral Exam, . Bingjing. Zhang. Outline. MapReduce. MapReduce. Frameworks. Iterative . MapReduce. Frameworks. Frameworks Based on . MapReduce. and Alternatives. MapReduce. Karim Ibrahim. Anh Pham. 1. Outline. Motivation & Introduction. Benchmarking . study. Optimizing . Hadoop. Motivation. 3. Motivation (. cont. ’). Motivation. :. MapReduce. model is not well suited for one pass analytics.. 2.0 and YARN. Subash. D’Souza. Who am I?. Senior Specialist Engineer at . Shopzilla. Co-Organizer for the Los Angeles . Hadoop. User group. Organizer for Los Angeles . HBase. User Group. Reviewer on Apache Flume: Distributed Log Processing, . Efficient and scalable architectures to perform pleasingly parallel, MapReduce and iterative data intensive computations on cloud environments. Thilina. . Gunarathne. (tgunarat@indiana.edu). Advisor : . HPC(Exascale) . Systems. June 24 2012. HPC . 2012 . Cetraro. , Italy. Geoffrey Fox. gcf@indiana.edu. . Informatics, Computing and Physics. Indiana . University . Bloomington. Science Computing Environments. Implications . for . Software Environments.  . eScience. in the Cloud . 2014. Redmond WA. April 30 2014. Geoffrey . Fox . gcf@indiana.edu. . . http://www.infomall.org. School of Informatics and Computing. ”. Cathy O’Neil & Rachel . Schutt. , 2013. R & Hadoop. Compute squares. 2. R. # create a list of 10 integers. ints. <- 1:10. # equivalent to . ints. <- c(1,2,3,4,5,6,7,8,9,10). # compute the squares. Jimmy Lin. The iSchool. University of Maryland. Monday, March 30, 2009. This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States. See http://creativecommons.org/licenses/by-nc-sa/3.0/us/ for details. Madhu M Nayak Assistant Professor, Department of C SE, GSSSIETW, Mysuru Pradeep.S Assistant Professor, Department of C SE, GEC, Kushal Nagar Abstract - Due to the increasing popularity of cheap 1 INTRODUCTIONImage processing is the frequently used technique in vast areas like medical image astronomical data analysisand so on The collection of images is increased to petabytes in last few Source. MapReduce. : Simplified Data Processing in Large Clusters. . Jefferey. Dean and Sanjay . Ghemawat. . OSDI 2004. Example Scenario. 3. Genome data from roughly . one million users. 125 MB of data per user.