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Comparing Implementation Features of Map Reduce in RDBMS with Distributed Cluster

Published on May 2015 by Mohammed Muddasir N, Ranjitha H C, Meghana S
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An Architectural Framework for Workload Demand Prediction in Scalable Federated Clouds
Foundation of Computer Science USA
ICCTAC2015 - Number 2
May 2015
Authors: Mohammed Muddasir N, Ranjitha H C, Meghana S
3514245c-6450-4ba6-ba0c-cc928c83c2dd

Mohammed Muddasir N, Ranjitha H C, Meghana S . Comparing Implementation Features of Map Reduce in RDBMS with Distributed Cluster. An Architectural Framework for Workload Demand Prediction in Scalable Federated Clouds. ICCTAC2015, 2 (May 2015), 19-24.

@article{
author = { Mohammed Muddasir N, Ranjitha H C, Meghana S },
title = { Comparing Implementation Features of Map Reduce in RDBMS with Distributed Cluster },
journal = { An Architectural Framework for Workload Demand Prediction in Scalable Federated Clouds },
issue_date = { May 2015 },
volume = { ICCTAC2015 },
number = { 2 },
month = { May },
year = { 2015 },
issn = 0975-8887,
pages = { 19-24 },
numpages = 6,
url = { /proceedings/icctac2015/number2/20928-2018/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 An Architectural Framework for Workload Demand Prediction in Scalable Federated Clouds
%A Mohammed Muddasir N
%A Ranjitha H C
%A Meghana S
%T Comparing Implementation Features of Map Reduce in RDBMS with Distributed Cluster
%J An Architectural Framework for Workload Demand Prediction in Scalable Federated Clouds
%@ 0975-8887
%V ICCTAC2015
%N 2
%P 19-24
%D 2015
%I International Journal of Computer Applications
Abstract

Data processing techniques are becoming more innovative as the amount of data grows. Here we are exploring such techniques to process big data one is the traditional RDBMS approach and the other distributed approach. We came across certain advantages and disadvantages of both the approaches. RDBMS is a very highly used technology for data processing by various organizations and replacing it with new technology has a lot of challenges. Distributed processing is the need of the hour and technologies like Hadoop, map reduce etc. [1] is being used for processing Big Data. There is a debate on which technology to use for processing data and we have just explored some possible results measuring both the technologies.

References
  1. Tom White |Hadoop: The Definitive Guide
  2. A community white paper developed by leading researchers across the United States | Challenges and Opportunities with Big Data
  3. DhrubaBorthakur |The Hadoop Distributed File System: Architecture and Design
  4. Xueyuan Su | Garret Swart |Oracle In-Database Hadoop: When Map Reduce Meets RDBMS
  5. Andrew Pavlo |Erik Paulson |Alexander Rasin |Daniel J. Abadi |David J. DeWitt |Samuel Madden |Michael Stonebraker |A Comparison of Approaches to Large-Scale Data Analysis
  6. Sarah Loebman |Dylan Nunley |YongChul Kwon |Bill Howe |Magdalena Balazinska |Jeffrey P. Gardner |Analyzing Massive Astrophysical Datasets: Can Pig/Hadoop or a Relational DBMS Help?
  7. Fei Chen |Meichun Hsu |A Performance Comparison of Parallel DBMSs and Map Reduce on Large-Scale Text Analytics
  8. Jeffrey Dean Sanjay Ghemawat |Map Reduce: Simplified Data Processing on Large Clusters
  9. http://hadoop. apache. org/docs/r0. 18. 0/hdfs_design. pdf
  10. http://lintool. github. io/MapReduceAlgorithms/MapReduce-book-final. pdf
  11. Dr. Hiren D. Joshi |Tapan P. Gondaliya |Big Data challenges and Hadoop as one of the solution of big data with its Modules
  12. http://www. mananing. com/lam/SampleCh10. pdf
Index Terms

Computer Science
Information Sciences

Keywords

Distributed Processing Rdbms Hadoop Map Reduce.