YouTube: big data analytics using Hadoop and map reduce

  • Authors

    • L Chandra Sekhar Reddy
    • Dr D. Murali
    2018-08-24
    https://doi.org/10.14419/ijet.v7i3.29.18451
  • Big Data Definition, Data Mining, YouTube Data Analysis, Hadoop, HDFS, Map Reduce, Unstructured Dataset Analysis.

  • Abstract

    We live today in a digital world a tremendous amount of data is generated by each digital service we use. This vast amount of data generated is called Big Data. According to Wikipedia, Big Data is a word for large data sets or compositions that the traditional data monitoring application software is pitiful to compress [5]. Extensive data cannot be used to receive data, store data, analyse data, search, share, transfer, view, consult, and update and maintain the confidentiality of information. Google's streaming services, YouTube, are one of the best examples of services that produce a massive amount of data in a brief period. Data extraction of a significant amount of data is done using Hadoop and MapReduce to measure performance. Hadoop is a system that offers consistent memory. Storage is provided by HDFS (Hadoop Distributed File System) and MapReduce analysis. MapReduce is a programming model and a corresponding implementation for processing large data sets. This article presents the analysis of Big Data on YouTube using the Hadoop and MapReduce techniques.

     

     


     

  • References

    1. [1] Webster, John. "MapReduce: Simplified Data Processing on Large Clusters", "Search Storage", 2004.

      [2] Bibliography: Big Data Analytics: Methods and Applications by SaumyadiptaPyne, B.L.S. Prakash Rao, And S.B. Rao.

      [3] YOUTUBE COMPANY STATISTICS. https://www.statisticbrain.com/youtube-statistics.

      [4] Youtube.com @2017. YouTube for media. https://www.youtube.com/yt/about/press.

      [5] Big data; Wikipedia https://en.wikipedia.org/wiki/Big_data.

      [6] Kallerhoff, Phillip. ―Big Data and Credit Unions: Machine Learning in Member Transactions https:// filene. Org/assets/pdfreports/301_Kallerhoff_M achine _Learning .pdf.

      [7] Marr,Barnard.―Why only one of the 5 Vs of significant data matters http:/ /www .ibmbigdatahub. Com /blog /why-only-one-5-vs- big-data-really-matters.

      [8] Information. "Chapter 1 - Big Data Overview". Big Data: Concepts, Methodologies, Tools, and Applications, Volume I. IGI Global. http:// common. Books 24x7. Com/toc.aspx?bookid=114 046.

      [9] Apache Hadoophttp://hadoop.apache.org/

      [10] How to Analyze Big Data with Hadoop technologies 3pillarglobal.com. 2017 https:/ /www. 3pillarglobal.com/insights/analyze-big-data-hadoop-technologies.

      [11] J. Dean, S. Ghemawat, MapReduce: Simplified Data Processing on Large Clusters, in OSDI‘04, 6th Symposium on Operating SystemsDesign and Implementation, Sponsored by USENIX, in cooperation with ACM SIGOPS, 2004, pp. 137– 150.

  • Downloads

  • How to Cite

    Chandra Sekhar Reddy, L., & D. Murali, D. (2018). YouTube: big data analytics using Hadoop and map reduce. International Journal of Engineering & Technology, 7(3.29), 12-15. https://doi.org/10.14419/ijet.v7i3.29.18451

    Received date: 2018-08-28

    Accepted date: 2018-08-28

    Published date: 2018-08-24