AI-Augmented Data Quality Monitoring in Real-Time Data Pipelines on AWS
TL;DR
An AI-augmented approach to data quality monitoring within real-time data pipelines on AWS is proposed that integrates machine learning models for anomaly detection, data drift analysis, and predictive quality assessment into the AWS streaming ecosystem.
Abstract
Ensuring high data quality is essential for the success of real-time data analytics, particularly in large-scale cloud environments such as AWS. Traditional rule-based monitoring approaches are often brittle, labor-intensive, and ill-suited for dynamic data patterns. This paper proposes an AI-augmented approach to data quality monitoring within real-time data pipelines on AWS. We present a reference architecture that integrates machine learning models for anomaly detection, data drift analysis, and predictive quality assessment into the AWS streaming ecosystem. The pipeline utilizes services such as Amazon Kinesis, AWS Lambda, SageMaker, and CloudWatch for scalable, low-latency data processing and observability. Through empirical evaluation, we demonstrate the effectiveness of AI-enhanced monitoring in identifying and mitigating quality issues with minimal human intervention, highlighting improvements in precision, recall, and operational efficiency. This approach not only improves trust in data-driven decisions but also offers a scalable solution for modern data engineering practices.