Evaluating the Impact of Event Hub Based Streaming Architectures on Enterprise Decision Latency
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V7I1P139Keywords:
Azure Event Hubs, Decision Latency, Stream Analytics, Change Data Capture, Aiops, Kappa Architecture, Real-Time Analytics, Enterprise Resource Planning, Business IntelligenceAbstract
In the contemporary hyper-competitive business landscape, the ability to transform raw data streams into actionable insights with minimal delay is a primary driver of strategic advantage. This research paper evaluates the systemic impact of Azure Event Hub–based streaming architectures on enterprise decision latency the temporal gap between a business event and the subsequent corrective or opportunistic action. By examining the integration of high-throughput ingestion via Azure Event Hubs, real-time transformation through Azure Stream Analytics, and continuous state synchronization via Change Data Capture (CDC), this study articulates a framework for reducing data and analysis latencies. Furthermore, the inclusion of Artificial Intelligence for IT Operations (AIOps) is analyzed as a mechanism for autonomous system maintenance and incident remediation. Drawing upon empirical benchmarks and industry success stories, including Netflix’s anomaly detection frameworks and Penguin Random House’s inventory optimization, the paper demonstrates that transitioning from traditional batch-oriented Lambda architectures to unified, streaming-first Kappa architectures can reduce decision latency by 17–25%. The findings suggest that such architectures not only improve operational key performance indicators (KPIs) by 9–14% but also fundamentally transform the role of the corporate strategist from an intuitive visionary to an empirical architect.
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