Published Paper PDF: https://ijrmeet.org/wp-content/uploads/2025/06/IJRMEET0625520059_Design%20Patterns%20for%20Multi-Tenant%20Data%20Lakehouse%20Architectures%20on%20Azure.pdf
DOI: https://doi.org/10.63345/ijrmeet.org.v13.i6.6
Lucky Jha
ABESIT
Crossings Republik, Ghaziabad, Uttar Pradesh 201009
Abstract
Multi-tenant data lakehouse architectures on Azure aim to provide scalable, secure, and cost-effective platforms where multiple independent tenants share the same underlying infrastructure while maintaining data isolation, governance, and performance guarantees. This manuscript explores the design patterns essential for implementing such architectures, emphasizing approaches for storage abstraction, workload isolation, metadata management, security controls, and cost optimization. We begin with an examination of the foundational concepts of data lakehouses and multi-tenancy, followed by a comprehensive literature review that surveys contemporary solutions and identifies gaps in existing platforms. The methodology section outlines a pattern-driven design process leveraging Azure Synapse Analytics, Azure Data Lake Storage Gen2, Delta Lake, and Azure Active Directory. In the results section, we detail a reference implementation, measure key performance indicators—including query latency, throughput, and cost per terabyte—and demonstrate how the chosen patterns address common multi-tenancy challenges. Finally, the conclusion synthesizes lessons learned, highlights best practices, and proposes directions for future research in scalable, secure multi-tenant lakehouse systems on Azure.
Keywords
Multi-tenancy; Data Lakehouse; Azure Synapse Analytics; Delta Lake; Tenant Isolation; Data Governance; Scalability
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