Automated Data Quality Monitoring in Streaming Data Transformations

Abstract

As real-time data becomes integral to decision-making, ensuring the quality of streaming data is critical to maintaining system reliability and business value. This paper explores automated data quality monitoring techniques specifically tailored for streaming data transformations. It investigates common data quality issues such as latency, duplication, schema drift, null values, and data inconsistency in real-time pipelines. The study presents a framework for integrating automated monitoring tools within streaming architectures using technologies like Apache Kafka, Apache Fink, and Spark Streaming. Machine learning-based anomaly detection models and rule-based engines are examined for detecting quality issues in-flight. Through experimental evaluation, the paper demonstrates how automated monitoring improves data observability, reduces downtime, and supports proactive data governance. The findings provide a roadmap for organizations seeking to implement scalable, real-time data quality solutions within their streaming ecosystems.