Research in Big Data Warehousing using Hadoop

Abderrazak Sebaa; Fatima Chikh; Amina Nouicer; Abdelkamel Tari

doi:10.20897/jisem.201710

Research in Big Data Warehousing using Hadoop

Abderrazak Sebaa ¹ ^* , Fatima Chikh ¹, Amina Nouicer ¹, Abdelkamel Tari ¹

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¹ LIMED laboratory, Computer Science Department, University of Bejaia, Bejaia, ALGERIA
^* Corresponding Author

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Research Article

Journal of Information Systems Engineering and Management, 2017 - Volume 2 Issue 2, Article No: 10
https://doi.org/10.20897/jisem.201710

Published Online: 30 Mar 2017

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APA 6th edition

In-text citation: (Sebaa et al., 2017)
Reference: Sebaa, A., Chikh, F., Nouicer, A., & Tari, A. (2017). Research in Big Data Warehousing using Hadoop. Journal of Information Systems Engineering and Management, 2(2), 10. https://doi.org/10.20897/jisem.201710

Vancouver

In-text citation: (1), (2), (3), etc.
Reference: Sebaa A, Chikh F, Nouicer A, Tari A. Research in Big Data Warehousing using Hadoop. J INFORM SYSTEMS ENG. 2017;2(2):10. https://doi.org/10.20897/jisem.201710

AMA 10th edition

In-text citation: (1), (2), (3), etc.
Reference: Sebaa A, Chikh F, Nouicer A, Tari A. Research in Big Data Warehousing using Hadoop. J INFORM SYSTEMS ENG. 2017;2(2), 10. https://doi.org/10.20897/jisem.201710

Chicago

In-text citation: (Sebaa et al., 2017)
Reference: Sebaa, Abderrazak, Fatima Chikh, Amina Nouicer, and Abdelkamel Tari. "Research in Big Data Warehousing using Hadoop". Journal of Information Systems Engineering and Management 2017 2 no. 2 (2017): 10. https://doi.org/10.20897/jisem.201710

Harvard

In-text citation: (Sebaa et al., 2017)
Reference: Sebaa, A., Chikh, F., Nouicer, A., and Tari, A. (2017). Research in Big Data Warehousing using Hadoop. Journal of Information Systems Engineering and Management, 2(2), 10. https://doi.org/10.20897/jisem.201710

MLA

In-text citation: (Sebaa et al., 2017)
Reference: Sebaa, Abderrazak et al. "Research in Big Data Warehousing using Hadoop". Journal of Information Systems Engineering and Management, vol. 2, no. 2, 2017, 10. https://doi.org/10.20897/jisem.201710

ABSTRACT

Traditional data warehouses have played a key role in decision support system until the recent past. However, the rapid growing of the data generation by the current applications requires new data warehousing systems: volume and format of collected datasets, data source variety, integration of unstructured data and powerful analytical processing. In the age of the Big Data, it is important to follow this pace and adapt the existing warehouse systems to overcome the new issues and challenges. In this paper, we focus on the data warehousing over big data. We discuss the limitations of the traditional ones. We present its alternative technologies and related future work for data warehousing

KEYWORDS

warehousing big data Hadoop Hive

REFERENCES

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.