Scalable Multi-Tenant Data Lakes with Zero Trust Security Architecture

Abstract

The exponential growth of enterprise data volumes necessitates scalable storage solutions that maintain stringent security controls across multi-tenant environments. Traditional data lake architectures exhibit significant vulnerabilities when deployed in shared infrastructure contexts, particularly regarding data isolation, access control granularity, and lateral movement prevention. This research presents a novel multi-tenant data lake architecture that integrates zero trust security principles with cloud-native scalability frameworks. Our proposed system implements dynamic micro-segmentation through software-defined networking overlays, identity-aware encryption mechanisms operating at the data block level, and fine-grained access policies enforced during data ingestion and query execution phases. The architecture leverages hybrid deployment across Hadoop Distributed File System and Snowflake cloud data warehouse platforms to optimize performance characteristics while maintaining security isolation boundaries. Comprehensive evaluation using synthetically generated datasets scaling to 10 petabytes demonstrates near-linear scalability properties with query latency increases remaining below 8% under high-concurrency workloads involving 1000+ simultaneous tenant operations. Statistical analysis reveals 99.97% data isolation effectiveness with zero cross-tenant data leakage incidents across 50,000 test scenarios. The proposed architecture addresses critical gaps in existing multi-tenant data lake security frameworks while providing practical deployment pathways for enterprise-scale implementations.