Data Transformation Methodologies between Heterogeneous Data Stores - A Comparative Study

Arnab Chakrabarti, Manasi Jayapal

2017

Abstract

With the advent of the NoSQL Data Stores, solutions for data migration from traditional relational databases to NoSQL databases is gaining more impetus in the recent times. This is also due to the fact that data generated in recent times are more heterogeneous in nature. In current available literatures and surveys we find that in-depth study has been already conducted for the tools and platform used in handling structured, unstructured and semi-structured data, however there are no guide which compares the methodologies of transforming and transferring data between these data stores. In this paper we present an extensive comparative study where we compare and evaluate data transformation methodologies between varied data sources as well as discuss the challenges and opportunities associated with it.

Paper Citation

in Harvard Style

Chakrabarti A. and Jayapal M. (2017). Data Transformation Methodologies between Heterogeneous Data Stores - A Comparative Study . In Proceedings of the 6th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-255-4, pages 241-248. DOI: 10.5220/0006438802410248

in Bibtex Style

@conference{data17,
author={Arnab Chakrabarti and Manasi Jayapal},
title={Data Transformation Methodologies between Heterogeneous Data Stores - A Comparative Study},
booktitle={Proceedings of the 6th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},
year={2017},
pages={241-248},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006438802410248},
isbn={978-989-758-255-4},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - Data Transformation Methodologies between Heterogeneous Data Stores - A Comparative Study
SN - 978-989-758-255-4
AU - Chakrabarti A.
AU - Jayapal M.
PY - 2017
SP - 241
EP - 248
DO - 10.5220/0006438802410248