  Real-world production systems often grapple with maintaining data quality in
large-scale, dynamic streams. We introduce Drifter, an efficient and
lightweight system for online feature monitoring and verification in
recommendation use cases. Drifter addresses limitations of existing methods by
delivering agile, responsive, and adaptable data quality monitoring, enabling
real-time root cause analysis, drift detection and insights into problematic
production events. Integrating state-of-the-art online feature ranking for
sparse data and anomaly detection ideas, Drifter is highly scalable and
resource-efficient, requiring only two threads and less than a gigabyte of RAM
per production deployments that handle millions of instances per minute.
Evaluation on real-world data sets demonstrates Drifter's effectiveness in
alerting and mitigating data quality issues, substantially improving
reliability and performance of real-time live recommender systems.
