P453: Cloud data warehouse migration methodology: Amazon Web Services study

Cloud data warehouse migration methodology: Amazon Web Services 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 Amazon Web Services (AWS). It asks a focused question: How should cloud data warehouse migration methodology be designed, governed, and empirically evaluated for AWS? To keep the discussion testable, cloud data warehouse migration methodology is defined in operational terms. The paper focuses on the points where architecture choices become visible in behavior—how data is represented, how policies are enforced, how workloads fail and recover, and what evidence is left behind. That boundary is intentionally narrower than a feature survey and broad enough to capture the system-level trade-offs. The paper contributes a decision framework and a validation plan. It connects architecture to governance, reliability, cost, and measurable evidence, and it uses design-science prototype and validation as the primary research method. Where no experiment has been run, the paper says so directly and specifies what would have to be measured before an empirical conclusion could be defended. Architectural Research Scope Research Domain / Theme: Migration, Benchmarking & Research Methods Architectural Scope: Amazon Web Services Core Research Question: How should cloud data warehouse migration methodology be designed, governed, and empirically evaluated for AWS? 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.