Citation:

Shires Gorgilli, 2026. Unified Data Modeling in GCP and AWS Environments for Retail Decision Systems,  Volume 6 Issue 3: 117-122.

Abstract:

Many retail organizations are finding that the Google Cloud Platform (GCP) and Amazon Web Services (AWS) “hybrid cloud” is now enabling them to scale and perform well on their decision systems. With their unique architecture, different storage models, and processing capabilities, however, they add fragmentation, duplicate and inefficiencies which could impact analytics-driven decision making. It outlines a unified data-modeling framework, backed by creating schema conventions, constraint and governability of data across clouds and making data interoperable when conducting retail analytics across clouds. It eliminates data redundancy, latency, supports compliance and provides time to insight and efficient operations by leveraging the necessary canonical schemas, federated query engines, standard formats and metadata-driven governance. This paper brought up light on the theoretical views of Unified modeling approach and suggested an improved modeling approach to implement the same in the retail world, which could help the retailers to have unified and parallel access to the similar compliant Analytics environment with GCP and AWS environments; thereby, supporting them in taking better decision making with adequate understanding.

References:

[1] George, J. (2022). Optimizing hybrid and multi-cloud architectures for real-time data streaming and analytics: Strategies for scalability and integration. World Journal of Advanced Engineering Technology and Sciences, 7(1), 10-30574.

[2] Pamisetty, A. (2022). A Comparative Study of AWS, Azure, and GCP for Scalable Big Data Solutions in Wholesale Product Distribution. Azure, and GCP for Scalable Big Data Solutions in Wholesale Product Distribution (December 28, 2022).

[3] Okra, B. P., Aduloju, T. D., Ajani, O. O., Ounce, O., & Amah, L. (2021). A cross-platform data mart synchronization model for high availability in dual-cloud architectures. Journal of Advanced Education and Sciences, 1(1), 70-77.

[4] Globe, L., & Kartashov, A. (2024). OPTIMIZING DISTRIBUTED DATA STORAGE IN MULTI-CLOUD ENVIRONMENTS: ALGORITHMIC APPROACH. Information and Telecommunication Sciences, (2), 4-12.

[5] Shoo, S. K. (2023). Open-source ETL Framework using Big Data tools Orchestration on AWS Cloud Platform.

[6] Mandala, N. R. (2022). Data Integration in Heterogeneous Systems. ESP Journal of Engineering & Technology Advancements, 2(4).

[7] Zhang, Q., Li, S., Li, Z., Xing, Y., Yang, Z., & Dai, Y. (2015). CHARM: A cost-efficient multi-cloud data hosting scheme with high availability. IEEE Transactions on Cloud computing, 3(3), 372-386.

[8] Pendyala, S. K. Cloud-Driven Data Engineering: Multi-Layered Architecture for Semantic Interoperability in Healthcare.

[9] Shade, F., & Schubert, P. (2020). A semantic data lake for harmonizing data from cross-platform digital workspaces using ontology-based data access.

[10] Dawson, C. (2023). Cloud Data Governance: Strategies for Managing Big Data in Distributed Environments.

[11] Ferula, S. (2023). The “Delta” Case: New AWS Data Platform Implementation (Doctoral dissertation, Polytechnic di Torino).

[12] Taylor, M., & Scott, A. (2021). Federated query processing in big data integration approaches and prospects. European Journal of Business and Innovation Research, 9(2), 79-87.

[13] Gleam, L. C., Kari, M. R., Zimmermann, L., Kohlbacher, O., Stinkhorn, H., Decker, S., & Bayan, O. (2020). Enabling ad-hoc reuse of private data repositories through schema extraction. Journal of biomedical semantics, 11(1), 6.

[14] Angamuthu, M. (2025). Optimizing Multi-Cloud Business Intelligence: A Framework for Balancing Cost, Performance, and Security. Journal of Computer Science and Technology Studies, 7(4), 427-437.

[15] Brora, P. (2024). Comparative Review: Top Cloud Service Providers ETL Tools-AWS vs. Azure vs. GCP. International Journal of Computer Engineering and Technology (IJCET) Volume, 15, 203-208.

[16] Ruíz-Ceniceros, J. A., Aguilar-Calderon, J. A., Tripp-Barb, C., & Zaldívar-Colado, A. (2023). Dynamic Canonical Data Model: An Architecture Proposal for the External and Data Loose Coupling for the Integration of Software Units. Applied Sciences, 13(19), 11040.

[17] Qi, Z., Ding, J., Javari, R. H., Denture, Y., Ming, X., & Tiara, P. (2023). Fed Sarah: A novel low-latency federated learning algorithm for consumer-centric personalized recommendation systems. IEEE Transactions on Consumer Electronics, 70(1), 2675-2686.

[18] Gunawardena, R. S. (2022). Dynamic Access Control Techniques and Their Role in Preserving Data Confidentiality in Multi-Cloud Retail Solutions. Journal of Computational Intelligence for Hybrid Cloud and Edge Computing Networks, 6(12), 12-22.

[19] Kodakandla, P. Cross-Cloud Data Engineering: Unified Big Data Workflows with AWS and GCP.

[20] Aquarelle, J. I., Asoka, A., Ojukwu, P. U., & Olajuwon, O. J. (2024). Data management solutions for real-time analytics in retail cloud environments. Engineering Science & Technology Journal, 5(11), 3180-3192.

[21] Kumar, R., Li, A., & Wang, W. (2018). Learning and optimizing through dynamic pricing. Journal of Revenue and Pricing Management, 17(2), 63-77.

[22] Kiss, E. (2025). Predictive Analytics for Cloud Resource Planning and Cost Forecasting.

[23] Roman, D., Nikola, N., Soil, A., Elvesæter, B., Song, H., Prod an, R., ... & Ceccarelli, R. (2021, September). Big data pipelines on the computing continuum: Ecosystem and use cases overview. In 2021 IEEE Symposium on Computers and Communications (ISCC) (pp. 1-4). IEEE.

[24] Adeyinka, A. (2023). Automated compliance management in hybrid cloud architectures: A policy-as-code approach.

[25] Wallace, F., MacKay, L., Anderson, C., & Martin, S. (2025). AI for Cross-Cloud Interoperability and Configuration Management.

[26] Arawak, R. (2024). The Role of Infrastructure as Code in Disaster Recovery and Business Continuity. Journal Homepage: http://www. Imran. us, 14(11).

[27] Peace, P., & Agora, H. (2024). Assessing the Role of Metadata in Data Governance Policies.

[28] Siring, A. (2018). Retail Fraud Detection via Log Analysis and Stream Processing. Computer Fraud & Security Bulletin, 2018, 21-32.

[29] Radhakrishnan, P., & Heath, R. (2021). Next-gen cloud synergy: Transforming finance, health, and retail through unified digital frameworks. International Journal of Business Management and Economic Review, 4(1), 1-10.

[30] Brawley, C. (2023). Understanding the Efficacy of Cloud Native Sensitive Data Protection Capabilities (Doctoral dissertation, Dublin, National College of Ireland).

Keywords:

Unified Data Modeling, Retail Analytics, GCP, AWS, Hybrid Cloud