Designing a Governed Lakehouse for Distributed Cloud Data and AI Systems: Practical Reference Architecture, Rollout Plan, and Trade-offs

Abstract

Enterprise lakehouse initiatives can stall when governance is treated as tool configuration and informal process rather than as a first-class platform capability. Modern lakehouse deployments introduce multiple enforcement surfaces (batch engines, interactive SQL, gateways, services) and typically require high availability within a region (for example, across availability zones) [1]. In some organizations, deployments also span regions for disaster recovery (DR) or cross-region access—creating governance-state drift, inconsistent policy interpretation, and expensive manual audit work if control-plane semantics are not explicit. This paper presents a governance-first reference architecture for distributed lakehouse environments, emphasizing separation of control-plane responsibilities from data-plane execution. It provides a practical blueprint for (i) authoritative dataset identity and lifecycle management, (ii) policy specification, versioning, and portability across engines, (iii) schema evolution and contract-based change management, (iv) audit-grade evidence capture and provenance, and (v) governance-state distribution semantics for multi-instance, multi-AZ, and DR scenarios. The article also includes operational acceptance checks and an adoption roadmap to guide phased implementation and prevent governance debt as platform adoption and AI usage expand [8].