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Engineering and Technology OPEN ACCESS

Cloud-Native Data Architecture: Optimizing Enterprise Analytics Using Multi-Cloud Data Warehousing Platforms

Md Ali Azam
PhD in Business Administration, University of the Cumberlands, 6178 College Station Drive, Williamsburg, KY 40769
Gurpreet Kaur
Master of Business Administration in International Business, Lincoln University, Oakland, California, USA
Keya Karabi Roy
Master of Science in Healthcare Management at St. FRANCIS COLLEGE, Brooklyn, New York
DOI Crossref DOI 10.37547/tajet/Volume08Issue09-06
tajet 2026
VOL. 8 / NO. 09 SEPTEMBER
FULL TEXT PP. 86-115
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VOLUME 8
ISSUE 09
YEAR 2026
PAGES 86-115
RESEARCH OVERVIEW

Abstract

With the rapid development of enterprise data ecosystems, scalable, flexible, high-performance architectures are needed to support advanced analytics and data-driven decision-making. On-premises and single-cloud data warehousing systems can find it difficult to meet the growing need for real-time processing, elastic scalability, cross-platform interoperability, and cost optimization. To meet these challenges, however, cloud-native architectures for data and multi-cloud data warehousing solutions have appeared as game-changers for contemporary enterprise analytics. This study explores how cloud-native architectural best practices, such as microservices, containers, serverless functions, and automated orchestration, can improve enterprise data environment performance and agility. Additionally, the paper examines the advantages of multi-cloud data warehousing approaches, such as the ability to harness the best attributes of various cloud providers, mitigate vendor lock-in threats, and enhance operational resilience. The study draws on a systematic review of the latest academic research, industry reports and enterprise case studies to collate evidence on the effectiveness of cloud-native and multi-cloud strategies for optimising analytical workloads, fast data integration, enhanced governance and AI-powered business intelligence initiatives. The results show significant benefits for organisations that embrace cloud-native data architecture in terms of scalability, deployment time, resource efficiency, and data analysis responsiveness. Multi-cloud data warehousing solutions also help enterprises optimize workload distribution, boost data availability and provide more support for real-time analytics across geographically scattered landscapes. The study finds that cloud-native data architecture can be an essential building block for next-generation enterprise analytics, and multi-cloud data warehousing platforms can deliver the flexibility needed for enterprise digital transformation. The paper provides a broad overview and synthesis of architectural approaches, business value, implementation hurdles and future perspectives for optimizing enterprise data management and analytics, adding to the expanding knowledge base about modern data infrastructure.

RESEARCH TOPICS

Keywords

Cloud-Native Data Architecture Multi-Cloud Computing Data Warehousing Enterprise Analytics Business Intelligence

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