Ankit Kumar Gupta1 & Ujjawal Jain2
1Uttar Pradesh Technical University, Lucknow, Uttar Pradesh, India ankitgupta.usa24@gmail.com
2Birmingham City University ,Cardigan St, Birmingham B4 7RJ, United Kingdom, jainujjawal117@gmail.com
Abstract
Designing scalable architectures for SAP data warehousing with BW Bridge integration is crucial for organizations seeking to modernize their data management systems and enhance analytical capabilities. SAP BW Bridge extends the capabilities of SAP Data Warehouse Cloud (DWC) by enabling seamless migration and integration of legacy SAP BW systems. This paper explores a scalable and efficient architecture design that leverages the strengths of BW Bridge to integrate traditional on-premises SAP BW landscapes with the cloud-native functionalities of SAP DWC. The proposed architecture outlines strategies for data ingestion, transformation, and consolidation using SAP BW Bridge as the intermediary. Key design principles focus on scalability, performance optimization, and operational simplicity. The integration enables organizations to leverage existing BW assets while transitioning to a modern, cloud-based data warehouse platform. Advanced features like data tiering, real-time data integration, and streamlined query execution are discussed to optimize cost and performance. This study also evaluates challenges such as data consistency, latency, and system complexity during the migration and operational phases. The paper highlights the role of automation tools, best practices for data modeling, and governance mechanisms to ensure a smooth and secure transition. Additionally, it addresses the need for scalability in handling growing data volumes and diverse analytical workloads. The findings demonstrate how BW Bridge integration empowers enterprises to retain historical investments in SAP BW while adopting scalable, cloud-driven data architectures. This hybrid approach paves the way for robust, future-ready analytics ecosystems that align with modern business needs.
Keywords
SAP BW Bridge, SAP Data Warehouse Cloud, scalable architecture, cloud migration, data integration, data modeling, performance optimization, real-time analytics, hybrid data warehousing, enterprise data management.
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- Sayata, Shachi Ghanshyam, Rahul Arulkumaran, Ravi Kiran Pagidi, Dr. S. P. Singh, Prof. (Dr.) Sandeep Kumar, and Shalu Jain. “Developing and Managing Risk Margins for CDS Index Options.” International Journal of Research in Modern Engineering and Emerging Technology 12(5):189. https://www.ijrmeet.org.
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- Ramachandran, R., Kshirsagar, R. P., Sengar, H. S., Kumar, D. L., Singh, D. S. P., & Goel, P. P. (2024). Optimizing Oracle ERP Implementations for Large Scale Organizations. Journal of Quantum Science and Technology (JQST), 1(1), 43–61. Link.
- Kendyala, Srinivasulu Harshavardhan, Krishna Kishor Tirupati, Sandhyarani Ganipaneni, Aman Shrivastav, Sangeet Vashishtha, and Shalu Jain. (2024). Optimizing PingFederate Deployment with Kubernetes and Containerization. International Journal of Worldwide Engineering Research, 2(6):34–50. Link.
- Ramachandran, Ramya, Ashvini Byri, Ashish Kumar, Dr. Satendra Pal Singh, Om Goel, and Prof. (Dr.) Punit Goel. (2024). Leveraging AI for Automated Business Process Reengineering in Oracle ERP. International Journal of Research in Modern Engineering and Emerging Technology (IJRMEET), 12(6):31. Retrieved October 20, 2024 (https://www.ijrmeet.org).
- Ramachandran, Ramya, Balaji Govindarajan, Imran Khan, Om Goel, Prof. (Dr.) Arpit Jain; Dr. Lalit Kumar. (2024). Enhancing ERP System Efficiency through Integration of Cloud Technologies. Iconic Research and Engineering Journals, Volume 8, Issue 3, 748-764.
- Ramalingam, B., Kshirsagar, R. P., Sengar, H. S., Kumar, D. L., Singh, D. S. P., & Goel, P. P. (2024). Leveraging AI and Machine Learning for Advanced Product Configuration and Optimization. Journal of Quantum Science and Technology (JQST), 1(2), 1–17. Link.
- Balachandar Ramalingam, Balaji Govindarajan, Imran Khan, Om Goel, Prof. (Dr.) Arpit Jain; Dr. Lalit Kumar. (2024). Integrating Digital Twin Technology with PLM for Enhanced Product Lifecycle Management. Iconic Research and Engineering Journals, Volume 8, Issue 3, 727-747.