Resilient and Scalable Data Lake Management Using Intelligent Orchestration for AI and Big Data

Abstract

The rapid convergence of artificial intelligence (AI), multimodal analytics, and large-scale data processing has intensified the need for data lake environments that can accommodate heterogeneous workloads while maintaining scalability, resilience, fairness, and operational efficiency. Conventional data lake management approaches often treat storage, processing, workload scheduling, and governance as relatively independent functions, which can create bottlenecks when AI workloads become dynamic and resource-intensive. This paper proposes a conceptual framework for resilient and scalable data lake management based on intelligent orchestration, in which workload characteristics, data-processing requirements, fairness constraints, and system resilience are jointly considered during resource and task coordination. The study develops its theoretical foundation by synthesizing the provided literature on fairness-aware learning, multimodal datasets, bias reduction, and representation learning, while connecting these principles to intelligent data-lake orchestration. Particular attention is given to multitenant environments in which concurrent AI and big-data workloads compete for computational and storage resources. The proposed approach emphasizes adaptive workload classification, fairness-aware resource allocation, resilience-oriented task management, and continuous orchestration. The analysis indicates that intelligent orchestration can provide a stronger conceptual basis for balancing scalability with reliability and responsible AI requirements. The findings further suggest that fairness should not be treated exclusively as a model-level concern but should also influence data and workload management decisions. The resulting framework offers a research direction for building adaptive data-lake infrastructures capable of supporting heterogeneous AI workloads without sacrificing operational resilience or responsible data utilization.