Adaptive Data Quality Monitoring: A Comprehensive Framework for Streaming Data Integrity

Real-time streaming pipelines are increasingly central to data-driven product decisions, yet the data quality literature has focused predominantly on batch systems. We present ADQM (Adaptive Data Quality Monitor), a framework for continuous, multi-dimensional quality monitoring of high-throughput event streams. ADQM evaluates four quality dimensions (completeness, freshness, accuracy, and consistency) over sliding windows of events, combines them into a composite score via the harmonic mean (which is uniquely sensitive to the weakest dimension), and applies Exponentially Weighted Moving Average (EWMA)-based statistical process control to separate genuine quality degradation from measurement noise. A Monte Carlo propagation module estimates downstream consumer impact from each detected defect. We evaluate ADQM on a synthetic large-scale streaming event stream (10,000 events per run, 11,574 events/second sustained throughput) across five defect types and 50 independent simulation runs. ADQM achieves 100% detection rate across all defect types, a false positive rate (FPR) of <inline-formula> <tex-math notation="LaTeX">$1.6\%\pm 0.8\%$ </tex-math></inline-formula>, a <inline-formula> <tex-math notation="LaTeX">$10.2\times $ </tex-math></inline-formula> improvement over the static <inline-formula> <tex-math notation="LaTeX">$z$ </tex-math></inline-formula>-score baseline (16.4%) and <inline-formula> <tex-math notation="LaTeX">$17.9\times $ </tex-math></inline-formula> over an Apache Griffin-style baseline (28.7%), while sustaining 46.9 million events/second throughput (<inline-formula> <tex-math notation="LaTeX">$4{,}058\times $ </tex-math></inline-formula> headroom over the target production rate). A hyperparameter sensitivity analysis over 1,500 additional simulation runs justifies the default configuration (<inline-formula> <tex-math notation="LaTeX">$\lambda {=}0.20$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$W{=}200$ </tex-math></inline-formula>). Component ablation with Wilcoxon signed-rank tests and Bonferroni correction confirms that the EWMA adaptive threshold is the primary driver of false positive reduction (Cohen&#x2019;s <inline-formula> <tex-math notation="LaTeX">$d=5.295$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$p\lt 0.001$ </tex-math></inline-formula>).