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Energy Efficient Green Cloud and AI Optimized Data Lake Architectures for Enterprise Digital Transformation

Anis Yazidi

Senior Systems Engineer, Switzerland

Abstract

Enterprise digital transformation has accelerated the adoption of cloud computing, big data analytics, and artificial intelligence (AI) across industries. While these technologies enable scalability, agility, and data-driven decision-making, they also introduce significant energy consumption and environmental challenges. Modern data centers contribute substantially to global electricity demand and carbon emissions, raising concerns about sustainability and operational costs. Energy-efficient green cloud computing combined with AI-optimized data lake architectures offers a promising pathway to balance performance, scalability, and environmental responsibility.

This research proposes a comprehensive architectural framework integrating green cloud principles with intelligent data lake optimization techniques to support enterprise digital transformation. The proposed framework leverages renewable-energy-aware cloud orchestration, carbon-aware workload scheduling, virtualization optimization, and AI-driven resource management to reduce energy footprints. Simultaneously, it integrates advanced data lake design patterns such as multi-tier storage, intelligent data lifecycle management, metadata-driven governance, and machine learning-based query optimization.

The study explores how AI models—particularly reinforcement learning, predictive analytics, and workload forecasting algorithms—can dynamically adjust computing resources, storage allocation, and cooling efficiency to minimize power consumption without compromising service-level agreements (SLAs). Furthermore, the architecture incorporates containerized microservices, serverless computing models, and edge-cloud hybrid deployments to improve computational efficiency. Data lake optimization techniques such as automated data partitioning, compression, deduplication, and adaptive indexing reduce storage overhead and accelerate analytics pipelines.

A methodological evaluation framework assesses energy consumption metrics, carbon intensity, data processing throughput, latency, and cost-efficiency across simulated enterprise workloads. Experimental findings indicate significant reductions in energy usage and infrastructure costs while maintaining high performance levels for AI-driven analytics.

The research concludes that integrating green cloud strategies with AI-optimized data lakes creates a sustainable, scalable, and intelligent foundation for enterprise digital ecosystems. By aligning environmental objectives with business performance metrics, organizations can achieve long-term digital transformation while meeting global sustainability targets and regulatory requirements.

Article Information

Journal

International Journal of Advanced Engineering Science and Information Technology (IJAESIT)

Volume (Issue)

Vol. 9 No. 1 (2026): International Journal of Advanced Engineering Science and Information Technology (IJAESIT)

DOI

https://doi.org/10.15662/IJAESIT.2026.0901005

Pages

31-42

Published

January 12, 2026
Copyright

Open Access

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Anis Yazidi (2026). Energy Efficient Green Cloud and AI Optimized Data Lake Architectures for Enterprise Digital Transformation. International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , Vol. 9 No. 1 (2026): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , pp. 31-42. https://doi.org/10.15662/IJAESIT.2026.0901005

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