International Journal on Science and Technology

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From Batch to Real-Time: Modernizing Enterprise Data Pipelines Using Azure Event Hub and Stream Analytics

Author(s) Pradeep Kachakayala
Country United States
Abstract The enterprise data landscape is currently undergoing a transformative shift from traditional batch-oriented Extract, Transform, and Load (ETL) processes to real-time, event-driven architectures. This transition is necessitated by the increasing demand for instantaneous insights in sectors such as high-frequency trading, industrial IoT monitoring, and personalized e-commerce. This research paper examines the modernization of enterprise data pipelines utilizing Azure Event Hubs and Azure Stream Analytics as the primary technological framework. It addresses the systemic challenges of latency, system fragmentation, and operational complexity inherent in legacy infrastructures. Through an exploration of architectural patterns like Lambda and Kappa, the paper provides practitioner-oriented insights into partitioning strategies, checkpointing mechanisms, and the enforcement of exactly-once semantics. Furthermore, the study delves into the critical requirements for schema governance, the implementation of data contracts, and the necessity of robust observability to manage continuous data flows. The integration of modern stream processing not only reduces decision latency but also facilitates a more agile, decentralized data management model, provided that the trade-offs between throughput, cost, and consistency are carefully managed.
Keywords Real-Time ETL, Azure Event Hubs, Azure Stream Analytics, Event-Driven Architecture, Data Pipeline Modernization, Schema Governance, Operational Reliability.
Field Engineering
Published In Volume 17, Issue 2, April-June 2026
Published On 2026-04-21
DOI https://doi.org/10.71097/IJSAT.v17.i2.11022
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