AI-Enhanced Ethernet–Lakehouse Convergence: Dynamic Bayesian Models and Real-Time Streaming Intelligence for SAP Workflows
DOI:
https://doi.org/10.15662/IJEETR.2025.0706017Keywords:
AI-Enhanced Lakehouse, Ethernet Convergence, Dynamic Bayesian Models, Real-Time Data Streaming, SAP Workflows, RAG-LLM, Cloud Computing, Data-Scarce Regions, Probabilistic Modeling, Anomaly Detection, Digital Transformation, Enterprise Data Architecture, Quality Assurance, Threat IntelligenceAbstract
The rapid growth of enterprise data demands architectures that unify low-latency connectivity with scalable analytical intelligence. This work introduces an AI-enhanced framework that converges Ethernet-level data transport with modern lakehouse architectures to optimize SAP-driven business processes. By integrating Dynamic Bayesian Hierarchical Models with real-time streaming pipelines, the system enables continuous probabilistic reasoning, anomaly detection, and adaptive decision-making across heterogeneous operational data sources. The proposed architecture leverages Retrieval-Augmented Generation (RAG), cloud-native orchestration, and intelligent quality assurance to transform SAP workflows into predictive, data-aware ecosystems. Results demonstrate improved data reliability, reduced system latency, and automated insights that enable organizations to operate effectively in both data-rich and data-scarce environments. This research highlights the strategic potential of merging networking intelligence, AI modeling, and lakehouse design to accelerate digital transformation across enterprise landscapes.
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