Comparative Analysis of Traditional Vs Cloud Data Warehouse
DOI:
https://doi.org/10.69591/jcai.v1i1.10Keywords:
Cloud Data warehouseAbstract
In recent years due to the emerging of technologies, data amount is getting increase day by day. The mode and methods of data handling are getting upgraded. Prediction analysis seems to be very difficult but it generates interesting results. Various sectors like health, transport, education and economics are providing large amount of data. Recent developments in web technologies made it possible for operator and researcher to analyze and forecast that huge amount of data. The domain of BI (business intelligence) is core technology which help its users to extract meaningful information for decision making regarding any business. Data warehouse offers an insight view of business processes using previously provided data. Still, this traditional data warehouse system is not suitable for data analysis as it doesn't meet the requirements of evolving industries of this age. It cannot provide the up or down scale. Plus, it cannot handle the growing number of users. A new form of data warehouse has developed having better design and features, known as cloud data warehouse. This system has evolved in recent years, which directly affects the business and application domain as well. It has evolved in such a way to control large-scale data and provide up and down scale of business anytime. Moreover, it can handle a large number of users. In this review paper, comparison of traditional and cloud data warehouse is provided.
References
S. Chaudhuri and U. Dayal, “An overview of data warehousing and OLAP technology,” ACM SIGMOD Rec., vol. 26, no. 1, pp. 65–74, Mar. 1997, doi: 10.1145/248603.248616.
W. H. Inmon, Building the data warehouse. John wiley & sons, 2005.
S. H. A. El-Sappagh, A. M. A. Hendawi, and A. H. El Bastawissy, “A proposed model for data warehouse ETL processes,” J. King Saud Univ.-Comput. Inf. Sci., vol. 23, no. 2, pp. 91–104, 2011.
H. Dabbèchi, A. Nabli, and L. Bouzguenda, “Towards Cloud-Based Data Warehouse as a Service for Big Data Analytics,” in Computational Collective Intelligence, vol. 9876, N. T. Nguyen, L. Iliadis, Y. Manolopoulos, and B. Trawiński, Eds., in Lecture Notes in Computer Science, vol. 9876. , Cham: Springer International Publishing, 2016, pp. 180–189. doi: 10.1007/978-3-319-45246-3_17.
U. Aftab and G. F. Siddiqui, “Big data augmentation with data warehouse: A survey,” in 2018 IEEE International Conference on Big Data (Big Data), IEEE, 2018, pp. 2785–2794. Available: https://ieeexplore.ieee.org/abstract/document/8622206/
S. Chaudhuri, U. Dayal, and V. Narasayya, “An overview of business intelligence technology,” Commun. ACM, vol. 54, no. 8, pp. 88–98, Aug. 2011, doi: 10.1145/1978542.1978562.
D. Agrawal, S. Das, and A. El Abbadi, “Big data and cloud computing: current state and future opportunities,” in Proceedings of the 14th International Conference on Extending Database Technology, Uppsala Sweden: ACM, Mar. 2011, pp. 530–533. doi: 10.1145/1951365.1951432.
L. Duan and L. Da Xu, “Business intelligence for enterprise systems: a survey,” IEEE Trans. Ind. Inform., vol. 8, no. 3, pp. 679–687, 2012.
H. Chen, R. H. Chiang, and V. C. Storey, “Business intelligence and analytics: From big data to big impact,” MIS Q., pp. 1165–1188, 2012.
A. Cuzzocrea, L. Bellatreche, and I.-Y. Song, “Data warehousing and OLAP over big data: current challenges and future research directions,” in Proceedings of the sixteenth international workshop on Data warehousing and OLAP, San Francisco California USA: ACM, Oct. 2013, pp. 67–70. doi: 10.1145/2513190.2517828.
E. Abdelaziz and O. Mohamed, “Optimisation of the queries execution plan in cloud data warehouses,” in 2015 5th World Congress on Information and Communication Technologies (WICT), IEEE, 2015, pp. 219–133. Available: https://ieeexplore.ieee.org/abstract/document/7489659/
D. J. Abadi, “Data management in the cloud: Limitations and opportunities.,” IEEE Data Eng Bull, vol. 32, no. 1, pp. 3–12, 2009.
M. Brantner, D. Florescu, D. Graf, D. Kossmann, and T. Kraska, “Building a database on S3,” in Proceedings of the 2008 ACM SIGMOD international conference on Management of data, Vancouver Canada: ACM, Jun. 2008, pp. 251–264. doi: 10.1145/1376616.1376645.
D. Lomet, A. Fekete, G. Weikum, and M. Zwilling, “Unbundling Transaction Services in the Cloud.” arXiv, Sep. 09, 2009.. Available: http://arxiv.org/abs/0909.1768
S. Das, D. Agrawal, and A. El Abbadi, “ElasTraS: An elastic, scalable, and self-managing transactional database for the cloud,” ACM Trans. Database Syst., vol. 38, no. 1, pp. 1–45, Apr. 2013, doi: 10.1145/2445583.2445588.
A. Aboulnaga, K. Salem, A. A. Soror, U. F. Minhas, P. Kokosielis, and S. Kamath, “Deploying database appliances in the cloud.,” IEEE Data Eng Bull, vol. 32, no. 1, pp. 13–20, 2009.
N. Paton, M. A. De Aragão, K. Lee, A. A. Fernandes, and R. Sakellariou, “Optimizing utility in cloud computing through autonomic workload execution,” Bull. Tech. Comm. Data Eng., vol. 32, no. 1, pp. 51–58, 2009.
J. Widom, “Research problems in data warehousing,” in Proceedings of the fourth international conference on Information and knowledge management - CIKM ’95, Baltimore, Maryland, United States: ACM Press, 1995, pp. 25–30. doi: 10.1145/221270.221319.
E. Guermazi, M. B. Ayed, and H. Ben-Abdallah, “Adaptive security for Cloud data warehouse as a service,” in 2015 IEEE/ACIS 14th International Conference on Computer and Information Science (ICIS), IEEE, 2015, pp. 647–650. Available: https://ieeexplore.ieee.org/abstract/document/7166672/
A. R. Nimje, “Data Analytics as a Service (DAaaS): An Arriving Technology in Cloud Computing,” Int. J. Emerg. Trend Eng. Basic Sci., vol. 2, no. 1, pp. 181–186, 2015.
S. Kurunji, T. Ge, B. Liu, and C. X. Chen, “Communication cost optimization for cloud Data Warehouse queries,” in 4th IEEE International Conference on Cloud Computing Technology and Science Proceedings, IEEE, 2012, pp. 512–519. Available: https://ieeexplore.ieee.org/abstract/document/6427580/
A. M. Van, H. L. Lv, V. L. Cheng, and F. V. Wang, “Design of cloud data warehouse and its application in smart grid,” in International Conference on Automatic Control and Artificial Intelligence (ACAI 2012), Xiamen, China: Institution of Engineering and Technology, 2012, pp. 849–852. doi: 10.1049/cp.2012.1108.
A. Abid et al., “A survey on search results diversification techniques,” Neural Comput. Appl., vol. 27, no. 5, pp. 1207–1229, Jul. 2016, doi: 10.1007/s00521-015-1945-5.
M. G. Kahn et al., “Migrating a research data warehouse to a public cloud: challenges and opportunities,” J. Am. Med. Inform. Assoc., vol. 29, no. 4, pp. 592–600, 2022.
L. Dinesh and K. G. Devi, “An efficient hybrid optimization of ETL process in data warehouse of cloud architecture,” J. Cloud Comput., vol. 13, no. 1, p. 12, Jan. 2024, doi: 10.1186/s13677-023-00571-y.
A. Nambiar and D. Mundra, “An overview of data warehouse and data lake in modern enterprise data management,” Big Data Cogn. Comput., vol. 6, no. 4, p. 132, 2022.
S. Rohajawati, “Effectiveness of Digital Transformation in Data Warehouse Technology,” Indones. J. Comput. Sci., vol. 13, no. 1, 2024. Available: http://ijcs.stmikindonesia.ac.id/ijcs/index.php/ijcs/article/view/3759
M. Patel and D. B. Patel, “Data Warehouse Modernization Using Document-Oriented ETL Framework for Real Time Analytics,” in Rising Threats in Expert Applications and Solutions, vol. 434, V. S. Rathore, S. C. Sharma, J. M. R. S. Tavares, C. Moreira, and B. Surendiran, Eds., in Lecture Notes in Networks and Systems, vol. 434. , Singapore: Springer Nature Singapore, 2022, pp. 33–41. doi: 10.1007/978-981-19-1122-4_5.
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