Immune-Inspired, Statistically-Validated Anomaly Detection for Data Quality Assurance in Large-Scale Data Engineering

Data quality assurance remains a critical challenge in large-scale data engineering, particularly during data migrations and transformations. Traditional SQL-based comparison methods are often inefficient, costly, and susceptible to human error. This paper introduces a novel immune-inspired anomaly detection algorithm, grounded in the negative selection algorithm (NSA) and enhanced with statistical validation. The Python-based implementation efficiently detects discrepancies between original and transformed datasets, scales to high-dimensional data, and reduces computational costs. Validation on multiple test cases using TPC-H benchmark data demonstrates robust performance with superior detection accuracy for structural anomalies (100%), date anomalies (75.08%), and value-based anomalies (49.16%), while maintaining excellent computational efficiency (processing 6 million rows in under 2 seconds). The approach is contextualized within the latest advances in data quality and anomaly detection, with comprehensive parameter sensitivity analysis and specialized strategies for detecting rare anomalies. Comparative evaluation against contemporary methods including Isolation Forest, LOF, DBSCAN, and autoencoders demonstrates the approach's complementary strengths and effectiveness across diverse anomaly types.