Big data: "Navigating the Hadoop ecosystem: Unraveling the potential of big data”

Authors

  • Saloni Kumari Ms.

DOI:

https://doi.org/10.14419/ijet.v12i1.32332

Published:

2023-09-06

Abstract

Big data refers to extremely large data sets that can be analyzed to reveal patterns, trends, and associations, particularly those relating to hu-man behavior and interactions. This data is too large and complex to be processed using traditional data processing methods, necessitating the use of specialized software and systems to manage and analyze it.

This paper discusses about the Big Data Architecture, Hadoop Ecosystem, HDFS, Map reduce, Yarn, Hive, and many other components of Hadoop ecosystem.

References

Adam, M. (2004). Why worry about theory-dependence? Circularity, minimal empiricality and reliability. International Studies in the Philosophy of Science, 18(2/3), 117–132. https://doi.org/10.1080/0269859042000296486.

Agrawal, R. and Srikant, R. (1994). Fast algorithms for mining association rules. Proceedings of the 20th International Conference on Very Large Data Bases, VLDB, 1215, 487–499.

Anderson, C. (2008). The end of theory: The data deluge makes the scientific method obsolete [Online]. Available at: http://archive.wired.com/science/discoveries/magazine/16-07/pb_theory [Accessed 11 May 2014].

Big data decision tree for continuous-valued attributes based on unbalanced cut points – SpringerOpen.

A systematic review on big data applications and scope for industrial processing and healthcare sectors – SpringerOpen.

View Full Article:

How to Cite

Saloni Kumari. (2023). Big data: "Navigating the Hadoop ecosystem: Unraveling the potential of big data”. International Journal of Engineering & Technology, 12(1), 7–10. https://doi.org/10.14419/ijet.v12i1.32332

License

Authors who publish with this journal agree to the following terms:

    1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution Licensethat allows others to share the work with an acknowledgement of the work''s authorship and initial publication in this journal.

    1. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal''s published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.

  1. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).