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Big Data & Data Science: A Descriptive Research on Big Data Evolution and a Proposed Combined Platform by Integrating R and Python on Hadoop for Big Data Analytics and Visualization

Published: 20 March 2020 Publication History

Abstract

In this technological era, Big Data is a new glorified term in where Data Science is the secret sauce of it. Undoubtedly, the digitalization of data is not the whole story; it is just a beginning of Data Science area of study. There was a time when the main focus was on building framework and processing of this data. After Hadoop HDFS and MapReduce resolved this issue already typically the concentration will follow to the next level. In terms of this, Big Data on Data Science becoming the most hyped solving area. At the moment of zettabytes data, R, Python, Hadoop all are in progressing phase in where integration among individual framework and tools will be highlighted and newest data handling tools are integrating with latest technology in terms of analytics competence. There will be a positivity when this integration will expose a new horizon for researchers and develop the preeminent solution based on the challenges.

References

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Foster Provost and Tom Fawcett. 2013. Data science and its relationship to big data and data driven decision making, ResearchGate, 22 pages.
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Vasant Dhar, Data Science and Prediction. 2013. Center for Digital Economy Research, Volume 56. 10 pages.
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Bogdan OANCEA, Raluca Mariana DRAGOESCU. 2014. Integrating R and Hadoop for Big Data Analysis, 12 pages.
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Xindong Wu, Xingquan Zhu, Gong-Qing Wu, Wei Ding. Data Mining with Big Data, 26 pages.
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Jagjit Kaur, Heena Girdher, 2018. HADOOP: A Solution to Big Data Problems using Partitioning mechanism map-Reduce, International Journal of Trend in Scientific Research and Development (IJTSRD), Volume 2, Issue 4, ISSN No: 2456 - 6470, 6 pages.
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  1. Big Data & Data Science: A Descriptive Research on Big Data Evolution and a Proposed Combined Platform by Integrating R and Python on Hadoop for Big Data Analytics and Visualization

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    ICCA 2020: Proceedings of the International Conference on Computing Advancements
    January 2020
    517 pages
    ISBN:9781450377782
    DOI:10.1145/3377049
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 20 March 2020

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    Author Tags

    1. Big Data
    2. Data Science
    3. Hadoop
    4. Python
    5. R

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