Designing a Cloud-Native Governance Model for Hyperscale Data Warehouse

Abstract

This thesis examines how a data governance model should be designed for a cloud-native, hyperscale data warehouse that holds the data of a public university. The topic originates from my workplace, where the migration of institutional data from a traditional on-premises warehouse to a cloud-native platform created a concrete need for a governance model. The subject is timely because a large number of organisations are currently carrying out the same migration and the existing data governance frameworks were developed for traditional enterprise data rather than for elastic, multi-tenant cloud platforms.

The thesis is a literature review. The research question is decomposed into six governance components: data ownership, data quality, access control, lineage, data lifecycle, and automation. Each component is analysed across the academic literature, the practitioner literature on cloud data warehouses, and published institutional data governance programmes. The material was gathered through a structured search of scientific databases and evaluated by classifying the contribution of each source to the components.

The result is a three-layer governance model in which every component is realised at a platform layer, a tooling layer, and an organisational layer. Additionally, these components are complemented by three cross-cutting design principles: policy-as-code automation, polycentric cross-organisational governance, and continuous monitoring. The analysis confirms that the classical Khatri–Brown decision-domain framework remains a compact and useful vocabulary, but shows that it must be extended with explicit lineage and automation dimensions and reconciled with the distributed authority and the dual institutional–research data of a public university.