Repository logo
Institutional Repository
Communities & Collections
Browse
Quick Links
  • Central Library
  • Digital Library
  • BHU Website
  • BHU Theses @ Shodhganga
  • BHU IRINS
  • Login
  • English
  • العربية
  • বাংলা
  • Català
  • Čeština
  • Deutsch
  • Ελληνικά
  • Español
  • Suomi
  • Français
  • Gàidhlig
  • हिंदी
  • Magyar
  • Italiano
  • Қазақ
  • Latviešu
  • Nederlands
  • Polski
  • Português
  • Português do Brasil
  • Srpski (lat)
  • Српски
  • Svenska
  • Türkçe
  • Yкраї́нська
  • Tiếng Việt
Log In
New user? Click here to register.Have you forgotten your password?
  1. Home
  2. Browse by Author

Browsing by Author "Sonali Agarwal"

Filter results by typing the first few letters
Now showing 1 - 1 of 1
  • Results Per Page
  • Sort Options
  • Loading...
    Thumbnail Image
    PublicationArticle
    Performance Analysis of Distributed Computing Frameworks for Big Data Analytics: Hadoop Vs Spark
    (Instituto Politecnico Nacional, 2020) Shwet Ketu; Pramod Kumar Mishra; Sonali Agarwal
    In the last one decade, the tremendous growth in data emphasizes big data storage and management issues with the highest priorities. For providing better support to software developers for dealing with big data problems, new programming platforms are continuously developing and Hadoop MapReduce is a big gamechanger followed by Spark, which sets the world of big data on fire with its processing speed and comfortable APIs. Hadoop framework emerged as a leading tool based on the MapReduce programming model with a distributed file system. Spark is on the other hand, recently developed big data analysis and management framework used to explore unlimited underlying features of Big Data. In this research work, a comparative analysis of Hadoop MapReduce and Spark has been presented based on working principle, performance, cost, ease of use, compatibility, data processing, failure tolerance, and security. Experimental analysis has been performed to observe the performance of Hadoop MapReduce and Spark for establishing their suitability under different constraints of the distributed computing environment. © 2020 Instituto Politecnico Nacional. All rights reserved.
An Initiative by BHU – Central Library
Powered by Dspace