AI-Enabled Data Lake Architecture for Efficient Multi-Tenant Big Data Orchestration

Abstract

The rapid expansion of heterogeneous data, machine learning workloads, and concurrent organizational requirements has increased the need for data lake architectures capable of supporting scalable, efficient, and intelligent multi-tenant orchestration. Conventional data lake designs primarily emphasize storage scalability, while comparatively less attention is given to adaptive workload management, tenant-aware resource allocation, exploration–exploitation decisions, and uncertainty-sensitive orchestration. This research develops a conceptual AI-enabled architecture for multi-tenant data lakes by integrating principles from reinforcement learning, multi-armed bandit optimization, dynamic programming, distributional learning, and entropy-based decision processes. The proposed architecture separates data ingestion, tenant isolation, metadata management, intelligent orchestration, resource allocation, and execution layers while using AI-based decision mechanisms to continuously adapt scheduling and resource policies. The theoretical foundation is derived exclusively from the supplied literature, including work on bandit optimization, dynamic programming, reinforcement learning, Monte-Carlo tree search, entropic regularization, and data-oriented learning environments. The architecture is positioned as a framework for improving workload placement, resource efficiency, fairness, and resilience in heterogeneous multi-tenant environments. The analysis indicates that combining adaptive exploration with value-based decision mechanisms can provide a stronger orchestration model than static policies, although computational overhead, training instability, tenant fairness, and limited empirical validation remain important constraints. The research contributes an integrated conceptual model for applying AI-driven decision intelligence to scalable multi-tenant data lake orchestration.