Moving Data Warehousing and Analytics to the Cloud to Improve Scalability, Performance and Cost-Efficiency
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V1I1P109Keywords:
Cloud computing, data warehousing, analytics, scalability, performance, cost-efficiency, cloud migration, cloud storage, big data analytics, hybrid cloud, real-time analytics, infrastructure optimization, data processing, flexibility, control, operational efficiency, resource optimization, large-scale data, smarter decision-making, real-time processing, data managementAbstract
Moving data warehousing & analytics to the cloud has completely changed how organizations handle their information. It has created a framework that is more flexible & scalable for the needs of modern businesses. Cloud platforms go beyond the limits of traditional on-premises systems by offering practically unlimited scalability, faster processing rates & more cost-effective solutions. This makes it easy for businesses to handle more data & more complex analytics. Using cloud-based data warehousing gives businesses access to cutting-edge technologies like serverless architectures, actual time analytics & easy connections to a wide range of these data sources. This greatly increases their ability to make many decisions & run their businesses more efficiently. This change is necessary to quickly adapt to changing many other workloads, improve performance & lower building expenses using the latest infrastructure. Still, moving to the cloud comes with these certain problems. Companies have to deal with many problems including data security, following the rules & the risks that come with being locked into a vendor. Strong encryption, rigorous access controls, and multi-cloud or hybrid solutions may help solve these problems. You need to apply the finest strategies to help the move go well. These include establishing a full migration strategy, completing thorough cost-benefit evaluations & concentrating on data governance. Actual world case studies highlight how companies in many other different sectors have used cloud-based analytics to get amazing outcomes, including getting insights quicker and finding the latest ways to make money. This paper speaks about how cloud computing is transforming the way we store & analyze their information. It talks about how it may help firms stay ahead of the competition for a long time by encouraging the latest ideas
References
[1] Lovas, R., Nagy, E., & Kovács, J. (2018). Cloud agnostic Big Data platform focusing on scalability and cost-efficiency. Advances in Engineering Software, 125, 167-177.
[2] Conley, M., Vahdat, A., & Porter, G. (2015, August). Achieving cost-efficient, data-intensive computing in the cloud. In Proceedings of the Sixth ACM Symposium on Cloud Computing (pp. 302-314).
[3] Muhammad, T., Munir, M. T., Munir, M. Z., & Zafar, M. W. (2018). Elevating Business Operations: The Transformative Power of Cloud Computing. International Journal of Computer Science and Technology, 2(1), 1-21.
[4] Shaik, Babulal. "Network Isolation Techniques in Multi-Tenant EKS Clusters." Distributed Learning and Broad Applications in Scientific Research 6 (2020).
[5] Guster, D. C., Brown, C. G., & Rice, E. P. (2018). Scalable Data Warehouse Architecture: A Higher Education Case Study. In Handbook of Research on Big Data Storage and Visualization Techniques (pp. 340-381). IGI Global.
[6] Patel, Piyushkumar, and Hetal Patel. "Developing a Risk Management Framework for Cybersecurity in Financial Reporting." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 1436-51.
[7] Balachandran, B. M., & Prasad, S. (2017). Challenges and benefits of deploying big data analytics in the cloud for business intelligence. Procedia Computer Science, 112, 1112-1122.
[8] Arugula, Balkishan, and Sudhkar Gade. “Cross-Border Banking Technology Integration: Overcoming Regulatory and Technical Challenges”. International Journal of Emerging Research in Engineering and Technology, vol. 1, no. 1, Mar. 2020, pp. 40-48
[9] Mansouri, Y., Toosi, A. N., & Buyya, R. (2017). Data storage management in cloud environments: Taxonomy, survey, and future directions. ACM Computing Surveys (CSUR), 50(6), 1-51.
[10] Sai Prasad Veluru. “Optimizing Large-Scale Payment Analytics With Apache Spark and Kafka”. JOURNAL OF RECENT TRENDS IN COMPUTER SCIENCE AND ENGINEERING ( JRTCSE), vol. 7, no. 1, Mar. 2019, pp. 146–163
[11] Nookala, G. (2020). Automation of privileged access control as part of enterprise control procedure. Journal of Big Data and Smart Systems, 1(1).
[12] Shee, H., Miah, S. J., Fairfield, L., & Pujawan, N. (2018). The impact of cloud-enabled process integration on supply chain performance and firm sustainability: the moderating role of top management. Supply Chain Management: An International Journal, 23(6), 500-517.
[13] Mohammad, Abdul Jabbar. “Sentiment-Driven Scheduling Optimizer”. International Journal of Emerging Research in Engineering and Technology, vol. 1, no. 2, June 2020, pp. 50-59
[14] Cheng, Y., Iqbal, M. S., Gupta, A., & Butt, A. R. (2015, June). Cast: Tiering storage for data analytics in the cloud. In Proceedings of the 24th international symposium on high-performance parallel and distributed computing (pp. 45-56).
[15] Jani, Parth. "Modernizing Claims Adjudication Systems with NoSQL and Apache Hive in Medicaid Expansion Programs." JOURNAL OF RECENT TRENDS IN COMPUTER SCIENCE AND ENGINEERING (JRTCSE) 7.1 (2019): 105-121.
[16] Manda, Jeevan Kumar. "AI And Machine Learning In Network Automation: Harnessing AI and Machine Learning Technologies to Automate Network Management Tasks and Enhance Operational Efficiency in Telecom, Based On Your Proficiency in AI-Driven Automation Initiatives." Educational Research (IJMCER) 1.4 (2019): 48-58.
[17] Strohbach, M., Daubert, J., Ravkin, H., & Lischka, M. (2016). Big data storage. New Horizons for a Data-Driven Economy: A Roadmap for Usage and Exploitation of Big Data in Europe, 119-141.
[18] Sai Prasad Veluru. “Real-Time Fraud Detection in Payment Systems Using Kafka and Machine Learning”. JOURNAL OF RECENT TRENDS IN COMPUTER SCIENCE AND ENGINEERING ( JRTCSE), vol. 7, no. 2, Dec. 2019, pp. 199-14
[19] Immaneni, J. (2020). Using Swarm Intelligence and Graph Databases Together for Advanced Fraud Detection. Journal of Big Data and Smart Systems, 1(1).
[20] Liu, C., Ranjan, R., Zhang, X., Yang, C., Georgakopoulos, D., & Chen, J. (2013, December). Public auditing for big data storage in cloud computing--a survey. In 2013 IEEE 16th International Conference on Computational Science and Engineering (pp. 1128-1135). IEEE.
[21] Manda, Jeevan Kumar. "Cloud Migration Strategies for Telecom Providers: Developing Best Practices and Considerations for Migrating Telecom Services and Infrastructure to Cloud-Based Environments." Available at SSRN 5003496 (2018).
[22] Balobaid, A., & Debnath, D. (2018). Cloud migration tools: Overview and comparison. In Services–SERVICES 2018: 14th World Congress, Held as Part of the Services Conference Federation, SCF 2018, Seattle, WA, USA, June 25–30, 2018, Proceedings 14 (pp. 93-106). Springer International Publishing.
[23] Jani, Parth. "UM Decision Automation Using PEGA and Machine Learning for Preauthorization Claims." The Distributed Learning and Broad Applications in Scientific Research 6 (2020): 1177-1205.
[24] Allam, Hitesh. Exploring the Algorithms for Automatic Image Retrieval Using Sketches. Diss. Missouri Western State University, 2017.
[25] Fu, Y., Qiu, X., & Wang, J. (2019, October). F2MC: Enhancing data storage services with fog-toMultiCloud hybrid computing. In 2019 IEEE 38th International Performance Computing and Communications Conference (IPCCC) (pp. 1-6). IEEE.
[26] Manda, Jeevan Kumar. "Cybersecurity strategies for legacy telecom systems: Developing tailored cybersecurity strategies to secure aging telecom infrastructures against modern cyber threats, leveraging your experience with legacy systems and cybersecurity practices." Leveraging your Experience with Legacy Systems and Cybersecurity Practices (January 01, 2017) (2017).
[27] Yang, C., Huang, Q., Li, Z., Liu, K., & Hu, F. (2017). Big Data and cloud computing: innovation opportunities and challenges. International Journal of Digital Earth, 10(1), 13-53.
[28] 28. Patel, Piyushkumar. "Navigating the TCJA’s Repatriation Tax: The Impact on Multinational Financial Strategies." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 1452-67.
[29] Immaneni, J. (2020). Building MLOps Pipelines in Fintech: Keeping Up with Continuous Machine Learning. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 1(2), 22-32.
[30] Abouelyazid, M., & Xiang, C. (2019). Architectures for AI Integration in Next-Generation Cloud Infrastructure, Development, Security, and Management. International Journal of Information and Cybersecurity, 3(1), 1-19.
[31] Han, H., Lee, Y. C., Choi, S., Yeom, H. Y., & Zomaya, A. Y. (2013, January). Cloud-aware processing of MapReduce-based OLAP applications. In Proceedings of the Eleventh Australasian Symposium on Parallel and Distributed Computing-Volume 140 (pp. 31-38).
