Data Quality Alerts and Pipeline Reliability in Urban Mobility Streams

Abstract

The exponential growth of urban mobility data streams has fundamentally transformed the operational capabilities of smart city infrastructures. However, maintaining the reliability of the complex data pipelines that process these streams remains a formidable challenge. Data quality alerts, which indicate anomalies such as missing values, schema drifts, or unexpected latency, are frequently generated but rarely analyzed in terms of their systemic impact on overall pipeline reliability. This paper investigates the relationship between localized data quality alerts and systemic pipeline failures using advanced graph analysis techniques. By modeling urban mobility data pipelines as directed acyclic graphs, we systematically evaluate how data quality degradation cascades through interdependent processing nodes. Through extensive simulation and empirical observation of urban mobility streams including vehicular telemetry and public transit ticketing events, we demonstrate that the topological properties of the pipeline graph significantly influence the propagation of quality errors. Furthermore, we introduce novel resilience metrics based on graph centrality and connectivity to predict downstream failures before they occur. Our findings indicate that analyzing data quality alerts through a graph-theoretical lens provides actionable insights into pipeline vulnerabilities, enabling data engineers to design more robust, fault-tolerant streaming architectures. This research bridges the gap between discrete data quality monitoring and holistic system reliability engineering.