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Graph-Based Denormalization for Migrating Big Data from SQL Database to NoSQL Database

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Intelligent Communication Technologies and Virtual Mobile Networks (ICICV 2019)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 33))

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Abstract

In this big data era, the data storing methods are vary based upon the data type and the technologies upgradation. Due to the increase of voluminous data, the traditional Relational Database Management Systems (RDBMS) are immature to handle the unstructured data. To overcome this issue, NoSQL databases are used to store and process the unstructured data. The big data migration from SQL to NoSQL database is more complex. The SQL databases are well-normalized database. Denormalization plays a major role in retrieving the data more efficiently. This work is carried on migrating the big data from SQL to NoSQL database using the Graph-based Denormalization method. The proposed method is more efficient for big data migration and post-migration process.

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Notes

  1. 1.

    https://datatechnologytoday.wordpress.com/2015/08/03/optimizing-database-performance-part-2-denormalization-and-clustering/.

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Authors and Affiliations

  1. Department of Computer Science, Mother Teresa Women’s University, Kodaikanal, Tamil Nadu, India

    V. Rathika

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  1. V. Rathika
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Correspondence to V. Rathika .

Editor information

Editors and Affiliations

  1. Department of CSE, Francis Xavier Engineering College, Tirunelveli, Tamil Nadu, India

    S. Balaji

  2. Departamento de Engenharia Informática, Faculdade de Ciências e Tecnologia, University of Coimbra, Coimbra, Portugal

    Álvaro Rocha

  3. Department of Electrical Engineering, National Changhua University of Education, Changhua County, Taiwan

    Yi-Nan Chung

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Rathika, V. (2020). Graph-Based Denormalization for Migrating Big Data from SQL Database to NoSQL Database. In: Balaji, S., Rocha, Á., Chung, YN. (eds) Intelligent Communication Technologies and Virtual Mobile Networks. ICICV 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 33. Springer, Cham. https://doi.org/10.1007/978-3-030-28364-3_56

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  • DOI: https://doi.org/10.1007/978-3-030-28364-3_56

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-28363-6

  • Online ISBN: 978-3-030-28364-3

  • eBook Packages: EngineeringEngineering (R0)

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