Distributed Multi-Cloud Data Lake and Edge Computing Architecture for Intelligent SAP Enterprise Data Integration
DOI:
https://doi.org/10.15680/IJCTECE.2023.0605012Keywords:
Distributed Data Lake, Multi-Cloud Architecture, SAP Data Integration, Edge Computing, Enterprise Data Analytics, Cloud-Native Infrastructure, Data Governance, Intelligent Data Processing, Enterprise Digital Ecosystems, Scalable Data PlatformsAbstract
Modern enterprises generate massive volumes of operational data from enterprise resource planning platforms, Internet of Things (IoT) devices, digital customer interactions, and distributed enterprise applications. Managing and integrating these diverse data sources efficiently has become one of the most significant challenges in digital enterprise environments. SAP enterprise systems remain central to business operations such as finance management, supply chain coordination, procurement, human resources, and customer relationship management. However, traditional centralized data management architectures often struggle to handle the velocity, variety, and scale of modern enterprise data streams. As organizations increasingly adopt hybrid and multi-cloud infrastructures, new architectural approaches are required to enable seamless data integration, real-time analytics, and scalable data processing across distributed environments. Data lake architectures combined with edge computing capabilities have emerged as promising solutions to address these challenges. This research proposes a distributed multi-cloud data lake and edge computing architecture designed to support intelligent SAP enterprise data integration. The proposed framework integrates cloud-native storage systems, distributed data processing platforms, and edge computing nodes to enable efficient data collection, transformation, and analysis across enterprise environments. The architecture leverages edge computing to perform preliminary data filtering and processing closer to data sources, reducing latency and network overhead while improving real-time decision-making capabilities. In addition, the multi-cloud data lake environment enables enterprises to store and analyze large volumes of structured and unstructured data generated by SAP applications and external enterprise systems. Machine learning–enabled data orchestration mechanisms are incorporated to optimize data ingestion, storage management, and analytics workflows across distributed cloud infrastructures. The research evaluates the effectiveness of the proposed architecture through simulated enterprise data integration scenarios involving large-scale SAP transaction data, edge device telemetry, and cloud-based analytics workloads. Experimental results demonstrate that the distributed multi-cloud data lake architecture significantly improves data processing efficiency, scalability, and integration performance compared with traditional centralized enterprise data platforms. The findings highlight the potential of combining multi-cloud data lakes with edge computing technologies to create intelligent enterprise data ecosystems capable of supporting advanced analytics, real-time decision-making, and digital transformation initiatives.
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