Implementation of Real-Time Event-driven data engineering using Azure Fabric with Stream Analytics

Abstract

The research examines the Azure Data Fabric, Event Hubs, and Stream Analytics that combine to apply event-driven data engineering in real time. Two major themes are identified, including the architectural fusion and scalability of Azure services, as well as performance trade-offs and decision support applied in real-time flows. The findings indicate that the suggested architecture enables near real-time and dataflow resilience with feasible scalability. This analysis can address the literature gaps by charting end-to-end integration issues and assessing its applicability in real-life areas. It makes the conclusion that configuration is complex, but the ecosystem of the Azure platform is a highly effective structure of real-time enterprise-scale data processing and analytics.