Building Secure Enterprise Data Lakes on Azure: Governance, Compliance, and Scalability Challenges
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V7I1P118Keywords:
Enterprise Data Lakes, Cloud-Native Architecture, Microsoft Azure, Governance-by-Design, Governance Debt, Zero-Trust Security Model, Identity-First Security, Data Compliance and Regulatory Governance, Data Lake Architecture, Microsoft Purview, Medallion Architecture, Data Mesh Architecture, Automated Data Governance, Cloud Security Engineering, Scalable Data Platforms, Enterprise Data Modernization, Intelligent Governance Systems, Agentic AI in Data ManagementAbstract
The contemporary enterprise landscape is characterized by a paradox of abundance: while organizations possess more data than ever before, their ability to extract secure, compliant, and scalable value from this asset is frequently compromised by architectural and organizational inertia. The shift toward cloud-native environments, specifically the Microsoft Azure platform, has provided the raw technological capability to store and process information at an unprecedented scale. However, the industry has observed a significant trend where enterprise data lake initiatives fail not due to the limitations of Azure’s services, but because of a pervasive reliance on legacy data management paradigms. These legacy methods, which emphasize perimeter-based security, manual governance, and monolithic storage patterns, are fundamentally incompatible with the requirements of modern, regulated, and high-growth environments. Addressing these failures requires a departure from incremental modernization. Instead, organizations must adopt a "governance-by-design" framework that treats security and compliance as foundational engineering disciplines rather than elective add-ons. This report explores the core challenges of building secure enterprise data lakes on Azure, introducing the concept of governance debt, detailing the transition to zero-trust and identity-first security models, and outlining the architectural patterns such as the Medallion and Data Mesh models that enable sustainable scalability. Furthermore, it examines the role of automated governance tools like Microsoft Purview and anticipates the evolution of these platforms into intelligent, agentic systems by 2030.
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