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Big Data Monitoring

In: Big Data Analytics

Author

Listed:
  • Ümit Demirbaga

    (University of Cambridge, Department of Medicine
    Bartin University, Department of Computer Engineering, Faculty of Engineering, Architecture, and Design)

  • Gagangeet Singh Aujla

    (Durham University, Department of Computer Science)

  • Anish Jindal

    (Durham University, Department of Computer Science)

  • Oğuzhan Kalyon

    (Newcastle University, Faculty of Medical Sciences)

Abstract

In this chapter, the reader is introduced to the pivotal domain of big data monitoring, delving into the fundamental concepts and tools essential for ensuring the seamless functioning of complex systems. The exploration begins by elucidating the different types of monitoring, namely proactive and reactive, and underscores the critical need for effective monitoring of big data systems. The chapter further dissects the components integral to monitoring, including alerts/notifications, events, logs, metrics, incidence tracking, and debugging capabilities. Providing a comprehensive overview, the narrative then outlines various available monitoring tools tailored for big data systems, ranging from DataDog and SequenceIQ to Sematext, Apache Chukwa, Nagios, Ganglia, DMon, and SmartMonit. By examining these tools, readers gain insights into the diverse functionalities and features contributing to efficient big data monitoring practices.

Suggested Citation

  • Ümit Demirbaga & Gagangeet Singh Aujla & Anish Jindal & Oğuzhan Kalyon, 2024. "Big Data Monitoring," Springer Books, in: Big Data Analytics, chapter 0, pages 155-170, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-55639-5_7
    DOI: 10.1007/978-3-031-55639-5_7
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