Autonomous Data Ecosystem: Self-Healing Architecture with Azure Event Hub and Databricks

Abstract

The rapid evolution of data processing demands has necessitated a paradigm shift from traditional batch-oriented systems to autonomous data ecosystems capable of self-monitoring, self-optimization, and self-healing. This article explores the architectural framework for building resilient real-time analytics systems using Azure Event Hub and Databricks, detailing how these technologies enable organizations to process massive data volumes with minimal latency while maintaining operational integrity. The article examines advanced machine learning models for predictive system behavior, including anomaly detection algorithms, reinforcement learning for resource optimization, and temporal pattern recognition in high-volume streams. Through implementations across financial services and logistics sectors, the article demonstrates significant improvements in processing efficiency, decision accuracy, and operational reliability compared to traditional approaches. The discussion addresses ethical considerations, emerging technologies, and research gaps while providing practical implementation recommendations for enterprises seeking to leverage autonomous data ecosystems for competitive advantage in dynamic business environments.