Skip to main content
SpringerLink
Log in
Book cover

Big Data in Engineering Applications pp 83–111Cite as

Concepts of HBase Archetypes in Big Data Engineering

  • Chapter
  • First Online:

Part of the book series: Studies in Big Data ((SBD,volume 44))

Abstract

All the technology that has been used for the big data handling is inspired by technology that was explain in the Google paper back in 2003. HBase is of the top most used and preferred open source distributed system developed by the Apache including apache zookeeper, apache Hadoop HBase provide random access for the storing and retrieving the data. In HBase we can store any type of data in any format, data can be structured and semi structured. It is very malleable and dynamic in case of data model. It is a No-SQL database i.e. it doesn’t let any inter row transactions to occur. Unlike traditional systems HBase run on multiple or a cluster of computers instead of single one, number of computer in a cluster can be increased or decreased as per the requirement. This type of design provide a more powerful and scalable approach for the data handling. This chapter explains about the how efficient HBase architecture and its command, operations are different from traditional systems.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Gopalani, S., & Arora, R. (2015). Comparing apache spark and map reduce with performance analysis using K-means. International Journal of Computer Applications, 113(1).

    Google Scholar 

  2. Wiewiórka, M. S., et al. (2014). SparkSeq: Fast, scalable, cloud-ready tool for the interactive genomic data analysis with nucleotide precision. Bioinformatics, btu343.

    Google Scholar 

  3. Shoro, A. G., & Soomro, T. R. (2015). Big data analysis: Apache spark perspective. Global Journal of Computer Science and Technology,15(1).

    Google Scholar 

  4. Gu, L., & Li, H. (2013). Memory or time: Performance evaluation for iterative operation on hadoop and spark. In 2013 IEEE 10th International Conference on High Performance Computing and Communications & 2013 IEEE International Conference on Embedded and Ubiquitous Computing (HPCC_EUC). IEEE.

    Google Scholar 

  5. Chen, H., et al. (2012). Hog: Distributed hadoop mapreduce on the grid. High Performance Computing, Networking, Storage and Analysis (SCC), 2012 SC Companion. IEEE.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Amity University, Noida, UP, India

    Ankur Saxena, Shivani Singh & Chetna Shakya

Authors
  1. Ankur Saxena
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shivani Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chetna Shakya
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ankur Saxena .

Editor information

Editors and Affiliations

  1. School of Computing Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

    Sanjiban Sekhar Roy

  2. Department of Civil Engineering, National Institute of Technology Patna, Patna, Bihar, India

    Pijush Samui

  3. University of Southern Queensland, Springfield, Queensland, Australia

    Ravinesh Deo

  4. Polytechnic University of Milan, Milan, Italy

    Stavros Ntalampiras

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Saxena, A., Singh, S., Shakya, C. (2018). Concepts of HBase Archetypes in Big Data Engineering. In: Roy, S., Samui, P., Deo, R., Ntalampiras, S. (eds) Big Data in Engineering Applications. Studies in Big Data, vol 44. Springer, Singapore. https://doi.org/10.1007/978-981-10-8476-8_5

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-8476-8_5

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-8475-1

  • Online ISBN: 978-981-10-8476-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation