The exponential growth of streaming data from diverse sources including Internet of Things devices, web applications, and database change data capture systems has created unprecedented challenges in data management, analytics, and governance. Traditional batch-oriented data architectures struggle to meet the demands of real-time analytics while maintaining data quality, security, and compliance requirements. This research presents a comprehensive cloud-native data analytics platform that integrates Apache Kafka for distributed messaging, Apache Flink for stream processing, Delta Lake for medallion architecture storage, and Feast feature store for machine learning operationalization, all unified under a robust governance framework leveraging Great Expectations, AWS security services, and enterprise observability tools. The proposed architecture processes over 340,000 events per second across multiple data sources, implements a three-tier medallion storage pattern with automated quality validation, and achieves sub-10-millisecond latency for online feature serving while maintaining point-in-time correctness for machine learning applications. Experimental validation demonstrates 99.95% data quality compliance, 99.99% system availability across three availability zones, and successful integration of 2,000+ feature definitions supporting both batch and streaming machine learning workloads. The platform addresses critical gaps in existing approaches by combining real-time stream processing with comprehensive data governance, automated quality remediation, and scalable feature engineering capabilities. This work contributes a production-ready reference architecture for organizations seeking to modernize their data infrastructure while maintaining enterprise-grade governance, security, and operational excellence standards.
Sandeep Kamadi (2023) studied this question.