Enterprise Data Warehousing In The Cloud Era: Strategies For Scalability, Analytics, And Bi Optimizationics
Keywords:
Data Warehousing, Cloud Analytics, Amazon Redshift, Business IntelligenceAbstract
In the contemporary era of exponential data generation, modern enterprises confront an unprecedented demand for robust, scalable, and flexible data warehousing solutions capable of supporting sophisticated business intelligence (BI) and analytics frameworks. This paper investigates the evolution of data warehousing architectures, the integration of cloud-based platforms, and the harmonization of big data paradigms to optimize organizational decision-making processes. Drawing upon foundational theories in data warehousing, including the seminal works of Inmon (2005) and Devlin (2020), the study evaluates contemporary cloud-enabled solutions such as Amazon Redshift, Snowflake, Teradata Vantage, and SAP Data Intelligence, highlighting their performance, scalability, and security characteristics (Worlikar, Patel, & Challa, 2025; Thakur & Sharma, 2022; Peng & Wang, 2023). The analysis contextualizes these technologies within the broader discourse on hybrid data architectures, encompassing Lambda, Kappa, and data lake frameworks (Lin, 2017; Miloslavskaya & Tolstoy, 2016; Giebler et al., 2018). Furthermore, the study examines empirical evidence of implementation challenges, including data integration complexity, compliance constraints, and organizational adoption hurdles. Through a comprehensive literature synthesis and descriptive interpretive analysis, the paper contributes to the critical understanding of strategic data warehouse deployment in global enterprises, proposing a nuanced conceptual model for integrating cloud, big data, and BI systems. The findings underscore the pivotal role of modern cloud data warehousing in enhancing operational agility, facilitating real-time analytics, and supporting evidence-based strategic decision-making. The study concludes by identifying research gaps, proposing areas for methodological innovation, and articulating future directions for scalable, secure, and adaptive data warehouse design.
References
Lin, J. The Lambda and the Kappa. IEEE Internet Comput. 2017, 21, 60–66.
Chandra, P.; Gupta, M.K. Comprehensive survey on data warehousing research. Int. J. Inf. Technol. 2018, 10, 217–224.
Garani, G.; Chernov, A.; Savvas, I.; Butakova, M. A Data Warehouse Approach for Business Intelligence. In Proceedings of the 2019 IEEE 28th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE), Napoli, Italy, 12–14 June 2019; pp. 70–75.
Thakur, V., & Sharma, R. Comparative Analysis of Cloud Data Warehousing Platforms. International Journal of Data Science and Analytics, 17(2), 101-119.
Gupta, V.; Singh, J. A Review of Data Warehousing and Business Intelligence in different perspective. Int. J. Comput. Sci. Inf. Technol. 2014, 5, 8263–8268.
Miloslavskaya, N.; Tolstoy, A. Application of Big Data, Fast Data, and Data Lake Concepts to Information Security Issues. In Proceedings of the 2016 IEEE 4th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW), Vienna, Austria, 22–24 August 2016; pp. 148–153.
SAP. (2024). SAP Data Intelligence: Data Integration and Optimization. Retrieved from https://www.sap.com/products/data-intelligence.html
Devlin, B. Thirty Years of Data Warehousing—Part 1. 2020. Available online: https://www.irmconnects.com/thirty-years-of-data-warehousing-part-1/
Peng, M., & Wang, X. (2023). Security and Compliance in Cloud Data Warehousing. Journal of Cybersecurity and Data Protection, 14(1), 56-73. https://doi.org/10.1016/j.jcs.2023.01.007
Snowflake. (2024). Snowflake Data Cloud: Performance and Scalability Features. Retrieved from https://www.snowflake.com/product/performance-and-scalability/
Al-Debei, M.M. Data Warehouse as a Backbone for Business Intelligence: Issues and Challenges. Eur. J. Econ. Financ. Adm. Sci. 2011, 33, 153–166.
Inmon, W.H. Building the Data Warehouse, 4th ed.; Wiley Publishing: Indianapolis, IN, USA, 2005.
Simões, D.M. Enterprise Data Warehouses: A conceptual framework for a successful implementation. In Proceedings of the Canadian Council for Small Business & Entrepreneurship Annual Conference, Calgary, AL, Canada, 28–30 October 2010.
Giebler, C.; Stach, C.; Schwarz, H.; Mitschang, B. BRAID—A Hybrid Processing Architecture for Big Data. In Proceedings of the 7th International Conference on Data Science, Technology and Applications, Porto, Portugal, 26–28 July 2018; pp. 294–301.
Worlikar, S., Patel, H., & Challa, A. (2025). Amazon Redshift Cookbook: Recipes for building modern data warehousing solutions. Packt Publishing Ltd.
Devlin, B. Thirty Years of Data Warehousing—Part 1. 2020.
Teradata. (2023). Optimizing Teradata Vantage for Big Data. Retrieved from https://www.teradata.com/Products/Cloud-Analytics/Cloud-Vantage
Report by Market Research Future (MRFR). Available online: https://finance.yahoo.com/news/data-warehouse-dwaas-market-predicted-153000649.html
Sagiroglu, S.; Sinanc, D. Big data: A review. In Proceedings of the 2013 International Conference on Collaboration Technologies and Systems (CTS), San Diego, CA, USA, 20–24 May 2013; pp. 42–47.
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Copyright (c) 2025 Prof. Alexei Kuznetsov
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