P452: Cloud data warehouse migration methodology: Microsoft Azure study

Cloud data warehouse migration methodology: Microsoft Azure study Author: Sonu Kumar Singh (Senior Consultant — Cloud & AI Solutions Architecture, Capgemini US LLC) Professional Credential: Member, IEEE (Membership # 102728576) | ORCID: 0009-0002-9180-4946 Abstract Cloud migration and comparative research are credible only when the workload definition, configuration, evidence collection, statistical method, failure cases, and reproducibility artifacts are explicit and independently auditable. Against this backdrop, the paper examines cloud data warehouse migration methodology in Microsoft Azure. It asks a focused question: How should cloud data warehouse migration methodology be designed, governed, and empirically evaluated for Microsoft Azure? The study narrows cloud data warehouse migration methodology to a small set of observable concerns rather than treating the topic as an umbrella term. The analysis identifies the design decisions that can be tested in an implementation, the assumptions that must be documented, and the failure modes that would invalidate an otherwise attractive architecture. This makes the research question concrete enough to support engineering evidence rather than opinion. Rather than declaring a winner, this study offers a repeatable way to reason about the problem. The method—controlled benchmark and statistical evaluation—is used to identify comparable responsibilities, likely trade-offs, and the evidence needed for validation. That distinction matters because managed cloud services change quickly, and a strong paper should make clear which statements come from documentation and which come from observed measurements. Architectural Research Scope Research Domain / Theme: Migration, Benchmarking & Research Methods Architectural Scope: Microsoft Azure Core Research Question: How should cloud data warehouse migration methodology be designed, governed, and empirically evaluated for Microsoft Azure? Specification Standard: Full 20-page peer-level monograph featuring system topology diagrams, 7 empirical benchmark tables, and failure-mode analyses. Published as part of the Cloud, AI, and Distributed Data Systems: 500-Monograph Engineering Corpus.