International Journal on Science and Technology

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Dynamic Data Orchestration: Enhancing Business Intelligence with Azure Data Factory

Author(s) Lokeshkumar Madabathula
Country United States
Abstract This article presents a comprehensive analysis of Azure Data Factory (ADF) as an enterprise-scale solution for dynamic data orchestration in modern business intelligence environments. Through examination of extensive implementation data across multiple organizations processing over 20 petabytes of data monthly, the article demonstrates how ADF's advanced features deliver significant improvements in data processing efficiency, reliability, and scalability. The article reveals that organizations utilizing ADF's component-based architecture achieve an average 78.3% reduction in pipeline development time and 91.2% decrease in maintenance overhead. The article further documents how intelligent scheduling mechanisms improve resource utilization by 78.6%, while comprehensive error handling frameworks reduce pipeline failures by 87.6%. The article findings indicate that ADF's integrated approach to data lineage tracking, automation, and governance enables organizations to handle data volume increases of up to 8.4x while maintaining 99.95% reliability and reducing operational costs by 31.5%. This article provides detailed implementation strategies and architectural patterns that have been validated across diverse enterprise environments.
Keywords Keywords: Dynamic Data Orchestration, Azure Data Factory (ADF), Enterprise Data Integration, Pipeline Automation, Data Governance and Lineage
Field Computer
Published In Volume 16, Issue 1, January-March 2025
Published On 2025-03-15
Cite This Dynamic Data Orchestration: Enhancing Business Intelligence with Azure Data Factory - Lokeshkumar Madabathula - IJSAT Volume 16, Issue 1, January-March 2025. DOI 10.71097/IJSAT.v16.i1.2286
DOI https://doi.org/10.71097/IJSAT.v16.i1.2286
Short DOI https://doi.org/g88scd
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