Cloud Data Warehousing and AI Analytics: A Comprehensive Review of Literature

  IJCTT-book-cover
 
         
 
© 2023 by IJCTT Journal
Volume-71 Issue-10
Year of Publication : 2023
Authors : Prashanth Kumar Mally
DOI :  10.14445/22312803/IJCTT-V71I10P104

How to Cite?

Prashanth Kumar Mally, "Cloud Data Warehousing and AI Analytics: A Comprehensive Review of Literature," International Journal of Computer Trends and Technology, vol. 71, no. 10, pp. 28-38, 2023. Crossref, https://doi.org/10.14445/22312803/IJCTT-V71I10P104

Abstract
This examination examines the progressive shifts in data management and analytics, spotlighting the migration from established systems like SAP BW to contemporary cloud data warehousing and AI analytics. It shows the obstacles emerging from rapid data proliferation and the cutting-edge solutions being developed in response. A detailed comparison unveils the amplified competencies and strategic edges associated with AI integration into cloud data warehousing. The review also scrutinizes unfolding trends, offering insights into the future landscape and expected influences on data management. The practical ramifications are dissected through case studies in diverse sectors, shedding light on the transformative essence of these innovations. Insights and recommendations are proffered, aiding in the navigation of intricate terrains and capitalization on emerging opportunities. Overall, the critical essence of continual learning and ingenuity in optimizing data for strategic gains is accentuated. This exhaustive review is tailored to be an invaluable asset for professionals and organizations striving to adapt to the swiftly transforming domain of data management and analytics.

Keywords
Data management, AI analytics, Cloud data warehousing, SAP BW, Data security.

Reference

[1] Amir Masoud Rahmani et al., “Artificial Intelligence Approaches and Mechanisms for Big Data Analytics: A Systematic Study,” PeerJ Computer Science, vol. 7, pp. 1-28, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Onkar Sharma, Sapient Undergoes Digital Metamorphosis, DataQuest, 2015. [Online]. Available: https://www.dqindia.com/sapient-undergoes-digital-metamorphosis/
[3] Mariam Anwar, The Future of AI in Data Warehousing: Trends and Predictions, Astera, 2023. [Online]. Available: https://www.astera.com/type/blog/ai-in-data-warehousing/
[4] Nikolaos-Alexandros Perifanis, and Fotis Kitsios, “Investigating the Influence of Artificial Intelligence on Business Value in the Digital Era of Strategy: A Literature Review,” Information, vol. 14, no. 2, pp. 1-42, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Christopher Collins, “Artificial Intelligence in Information Systems Research: A Systematic Literature Review and Research Agenda,” International Journal of Information Management, vol. 60, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Arun Varadarajan, Evolution of Data Warehousing on the SAP Platform-The Road to BW4/HANA, Visualbi, 2017. [Online]. Available: https://visualbi.com/blogs/business-intelligence/sap-data-warehousing-annotated-timeline/
[7] Oliver Huth, SAP Data Warehouse Cloud, Data Marketplace: An Overview, SAP, 2021. [Online]. Available: https://blogs.sap.com/2021/12/13/sap-data-warehouse-cloud-data-marketplace-an-overview/
[8] Roger McHaney, Cloud Technologies: An Overview of Cloud Computing Technologies for Managers, Wiley, pp. 1-288, 2021.
[Google Scholar] [Publisher Link]
[9] Virginia Backaitis, SAP Unleashes SAP BW/4HANA, A Data Warehouse for the Digital Era, Cmswire, 2016. [Online]. Available: https://www.cmswire.com/analytics/sap-unleashes-sap-bw4hana-a-data-warehouse-for-the-digital-era/
[10] A.R. Guess, SAP Modernizes Data Warehousing with the Launch of SAP BW/4HANA, Dataversity, 2016. [Online]. Available: https://www.dataversity.net/sap-modernizes-data-warehousing-launch-sap-bw4hana/
[11] E. Kesavulu Reddy, “The Analytics of Clouds and Big Data Computing,” SSRG International Journal of Computer Science and Engineering, vol. 3, no. 11, pp. 31-35, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[12] H.L. Lv et al., “Design of Cloud Data Warehouse and its Application in Smart Grid,” International Conference on Automatic Control and Artificial Intelligence, pp. 849-852, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Athira Nambiar, and Divyansh Mundra, “An Overview of Data Warehouse and Data Lake in Modern Enterprise Data Management,” Big Data and Cognitive Computing, vol. 6, no. 4, pp. 1-24, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Sérgio Fernandes, and Jorge Bernardino, “Cloud Data Warehousing for SMEs,” International Joint Conference on Software Technologies, pp. 276-282, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Kun Liu, and Long-jiang Dong, “Research on Cloud Data Storage Technology and its Architecture Implementation,” Procedia Engineering, vol. 29, pp. 133-137, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Rajesh Francis, Rajiv Gupta, and Milind Oke, Amazon Redshift: The Definitive Guide, O'Reilly Media, pp. 1-458, 2023.
[Publisher Link]
[17] BigQuery’s Performance Powers Auto Trader UK’s Real-Time Analytics, Google Cloud, 2022. [Online]. Available: https://cloud.google.com/blog/products/data-analytics/bigquery-performance-powers-real-time-analytics
[18] Nelson Sizwe Madonsela, Paulin Mbecke, and Charles Mbohwa, “Integrating Artificial Intelligence into Data Warehousing and Data Mining,” Proceedings of the World Congress on Engineering and Computer Science, vol. 2, pp. 1-5, 2015.
[Google Scholar] [Publisher Link]
[19] Adnane Drissi Elbouzidi et al., “The Role of AI in Warehouse Digital Twins: Literature Review,” Applied Sciences, vol. 13, no. 11, pp. 1-21, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Leo Willyanto Santoso, and Yulia, “Data Warehouse with Big Data Technology for Higher Education,” Procedia Computer Science, vol. 124, pp. 93-99, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Abdulaziz Aldoseri, Khalifa N. Al-Khalifa, and Abdel Magid Hamouda, “Re-Thinking Data Strategy and Integration for Artificial Intelligence: Concepts, Opportunities, and Challenges,” Applied Sciences, vol. 13, no. 12, pp. 1-33, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[22] M. Asif Naeem, Saif Ullah, and Imran Sarwar Bajwa, Interacting with Data Warehouse by Using a Natural Language Interface, Natural Language Processing and Information Systems, pp. 372-377, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Aws Al-Okaily et al., “An Empirical Study on Data Warehouse Systems Effectiveness: The Case of Jordanian Banks in the Business Intelligence Era,” EuroMed Journal of Business, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[24] François Bocquet, Mario Campone, and Marc Cuggia, “The Challenges of Implementing Comprehensive Clinical Data Warehouses in Hospitals,” International Journal of Environmental Research and Public Health, vol. 19, no. 12, pp. 1-6, 2022.
[CrossRef] [Google Scholar] [Publisher Link]