Real-Time Data Quality Monitoring Frameworks for High-Velocity Streaming Pipelines

The fast growth of data generated by IoT-enabled devices, cloud apps, financial transactions, social media networks, and various sensors used for monitoring purposes necessitates constant monitoring of data quality in real-time streaming environments. Poor quality data negatively influences the performance and validity of analysis, decision-making processes, system performance, and businesses' profitability. Conventional data quality evaluation frameworks designed for batch data environments often prove insufficient when implemented in today's streaming environments due to the inability to monitor and fix potential quality problems in a timely manner. The current research explores the state-of-the-art approaches to monitoring data quality in high-velocity streaming contexts and discusses major data quality assurance methodologies. In particular, the suggested monitoring framework includes continuous validation and anomaly detection techniques, as well as the monitoring of the data schema, missing values, and alert-based monitoring. The methodology applied in this paper involved a literature review of existing scholarly studies on high-velocity data quality monitoring in real-time stream processing frameworks. The results show that real-time data quality monitoring greatly improves data reliability, eliminates potential errors and their spread, improves decision-making efficiency, and stream processing scalability.