P115: Warehouse-lake convergence: Portable multi-cloud study

Warehouse-lake convergence: Portable multi-cloud 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 Warehouse-lake convergence sits at a point where cloud architecture, data engineering, and AI operations increasingly overlap. That overlap creates real opportunities, but it also makes design choices harder to evaluate: a convenient managed feature can improve delivery speed while changing portability, governance, or failure behavior. This paper examines warehouse-lake convergence for a portable design spanning Azure, AWS, and Google Cloud, with the goal of separating enduring architectural principles from provider-specific implementation choices. Here, the topic is treated as a set of engineering obligations, not a slogan. The important questions are what must remain correct under scale and failure, which controls must follow the workload across services, and which measurements would be needed to support a claim. Framing the topic this way prevents the analysis from reducing to a list of vendor capabilities. The main contribution is a structured way to move from architectural claims to evidence. Using controlled benchmark and statistical evaluation, the paper links design choices with security controls, operating assumptions, economics, and reproducibility. It does not fill gaps with synthetic benchmark numbers; instead, it defines the experiment that would be required to turn a design hypothesis into a supported result. Architectural Research Scope Research Domain / Theme: Lakehouse & Analytics Architectural Scope: Portable multi-cloud Core Research Question: How should warehouse-lake convergence be designed, governed, and empirically evaluated for a portable multi-cloud design spanning Azure, AWS, and Google Cloud? 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.