PPT-parallel dbms vs map reduce

MapReduce and parallel DBMSs friends or foes Stonebraker Daniel Abadi David J Dewitt et al MAP REDUCE AND PARALLEL DBMS ARE COMPLEMENTARY In 2010 MapReduce MR has been hailed as a . Xtreme Compression, Inc. is a software design consultancy with special expertise in structured data compression. We apply our proprietary algorithms and source code to compress and encrypt structured data whenever performance beyond the reach of conventional methods is required. Xtreme Compression, Inc. is a software design consultancy with special expertise in structured data compression. We apply our proprietary algorithms and source code to compress and encrypt structured data whenever performance beyond the reach of conventional methods is required. 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 . Databases and Database Management Systems. Lecturer: Akanferi Albert. akanferi@yahoo.com. 1. Essential Areas of Part 1. Problems with File-based System. Advantages Offered by Database Approach. Database Environment. Data Models. Record-Based Data Models. In a record-based model, the database consists of a number of fixed-format records possibly of differing types. . Each record type defines a fixed number of fields, each typically of a fixed length. . Arvind Arasu, Ken Eguro, . Ravi Ramamurthy, Raghav Kaushik. Microsoft Research. Cloud Computing . 2. Well-documented benefits. Trend to move computation and data to cloud. Database functionality. Amazon RDS. : Transparent High Availability for Database Systems. Umar . Farooq. Minhas. 1. , . Shriram. Rajagopalan. 2. , Brendan Cully. 2. ,. Ashraf Aboulnaga. 1. , Kenneth Salem. 1. , Andrew Warfield. 2. The Need for High . Large Scale Duplicate Detection. Prof. Felix . Naumann. , . Arvid. . Heise. Agenda. Big Data. Word Count Example. Hadoop. Distributed File System. Hadoop. . Map/Reduce. Advanced . Map/Reduce. Stratosphere. Chen Shapira. http://prodlife.wordpress.com. B.Sc. . in CS and . Statistics. OCP. 10 . years of production IT . experience. Oracle Ace. Five successful Streams implementations last year. .. Goal:. . 1. Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe. Chapter 2. Database System Concepts and Architecture. Slide 2- . 3. Outline. Data Models and Their Categories. History of Data Models. Schemas, Instances, and States. ish. ) Frameworks. William Cohen. 1. Outline. More concise languages for map-reduce pipelines. Abstractions built on top of map-reduce. General comments. Specific systems. Cascading, Pipes. PIG, Hive. © . 2013 . Pearson Education, Inc.  Publishing as Prentice Hall. 1. Modern Database Management. 11. th. Edition. Jeffrey A. Hoffer, V. Ramesh, . Heikki Topi. . Objectives. Define terms. Explain business conditions driving distributed databases. 1. Word Count Example. We have a large file of words, one word to a line. Count the number of times each distinct word appears in the file. Sample application: analyze web server logs to find popular URLs. 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