Optimizing Delta Load Processes from SAP HANA to Microsoft Fabric (One Lake) Using Azure Data Factory (ADF) and Database Triggers

Title: Optimizing Delta Load Processes from SAP HANA to Microsoft Fabric (One Lake) Using Azure Data Factory (ADF) and Database Triggers Author: Govinda Rao Banothu – SAP Analytics Cloud Technical Specialist, NJ, USA Publication Details: Journal: American Journal of Engineering Research (AJER) Volume: 13, Issue 11, 2024 ISSN: e-ISSN: 2320-0847 | p-ISSN: 2320-0936 DOI: [Provide if available] Abstract: As businesses increasingly require real-time data integration, optimizing delta load processes becomes a critical necessity. This paper presents a structured framework for efficiently extracting and transferring only modified data from SAP HANA to Microsoft Fabric (One Lake) using Azure Data Factory (ADF) and database triggers. The approach enhances data movement efficiency, reduces bandwidth usage, and improves system performance compared to traditional full data loads. Key highlights include:✅ Database Triggers in SAP HANA for efficient Change Data Capture (CDC).✅ ADF Pipelines for orchestrating automated data transfer and transformation.✅ Delta vs. Full Load Comparison, proving improved latency, consistency, and resource optimization.✅ Real-World Case Studies demonstrating significant performance improvements and cost savings.✅ Overcoming SAP’s ODP (Operational Delta Provisioning) Restrictions using alternative methodologies. Key Takeaways: Enhanced Data Processing Efficiency: Extracts only changed records, reducing ETL workload and processing time. Scalable & Cloud-Optimized Architecture: Seamless integration with Microsoft Fabric (One Lake) for modern data platforms. Automated & Reliable Data Pipelines: Improved data consistency, governance, and automation for enterprise analytics. Business Impact: Faster decision-making insights, lower cloud storage costs, and real-time reporting enhancements. Future Scope: Implementation of real-time replication pipelines from SAP HANA to cloud environments. Automation of data pipeline generation leveraging metadata-driven workflows. Enhancing logging table cleanup strategies for efficient long-term data management.