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Optimizing Multi-Cloud Data Engineering Strategies with Azure for Cross-Industry Operations: A Federated Data Processing Approach

Author(s) Urvangkumar Kothari
Country United States
Abstract As enterprises expand their cloud strategies, multi-cloud and hybrid-cloud architectures have become critical for data engineering and analytics. This paper presents Azure-based methodologies for enabling federated data processing across AWS, GCP, and on-premise infrastructures. We explore how Azure Synapse Analytics, Azure Arc, and Data Factory facilitate cross-cloud data pipelines, ensuring performance, security, and compliance. Case studies from Manufacturing, Gaming, and Dairy industries illustrate real-world challenges and solutions in multi-cloud data engineering. The proposed federated data processing approach minimizes data movement while maximizing analytical capabilities and reducing operational overhead by 37%. Implementation results demonstrate significant improvements in predictive maintenance (74% reduction in downtime), fraud detection (28% reduction in losses), and demand forecasting (18% improvement in accuracy) across the studied industries.
Keywords Multi-cloud, Azure, Data Engineering, Federated Data Processing, Hybrid-cloud, Azure Synapse Analytics, Azure Arc, Data Factory
Field Engineering
Published In Volume 3, Issue 7, July 2022
Published On 2022-07-06
Cite This Optimizing Multi-Cloud Data Engineering Strategies with Azure for Cross-Industry Operations: A Federated Data Processing Approach - Urvangkumar Kothari - IJLRP Volume 3, Issue 7, July 2022. DOI 10.70528/IJLRP.v3.i7.1642
DOI https://doi.org/10.70528/IJLRP.v3.i7.1642
Short DOI https://doi.org/g9t8k3
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