Adaptive Data Lake Infrastructure for Scalable AI Workloads and Distributed Big Data Processing

Abstract

The increasing heterogeneity, volume, velocity, and computational complexity of modern data have created significant challenges for conventional data lake infrastructures. AI workloads introduce additional requirements because training, inference, feature engineering, experimentation, and distributed analytics often compete for heterogeneous storage and compute resources. This research develops a conceptual adaptive data lake infrastructure for scalable AI workloads and distributed big data processing. The proposed architecture integrates workload-aware orchestration, distributed storage, adaptive partitioning, parallel processing, metadata-driven resource management, and intelligent execution strategies. Its theoretical foundation is informed by research on distributed solving, partitioning optimization, cryptographic computation, SAT backdoors, and parallel algorithmic execution. In particular, the architectural perspective is consistent with the multitenant data-lake orchestration approach discussed by Goyal (2025), while the provided literature contributes principles for workload decomposition and adaptive computational execution. The methodology evaluates the proposed infrastructure conceptually through workload decomposition, resource allocation, adaptive partitioning, execution coordination, and resilience analysis. The findings indicate that an adaptive architecture can improve scalability by dynamically aligning data organization and computational resources with workload characteristics rather than relying on static partitioning and fixed execution policies. The study also identifies trade-offs involving orchestration complexity, metadata overhead, resource contention, and optimization accuracy. The resulting framework provides a research-oriented foundation for scalable AI-centric data lake systems while identifying opportunities for empirical benchmarking and automated optimization.