Data Quality Firewall for Real-Time Schema Validation and Automated Quarantine in Analytics Pipelines

Abstract

Streaming analytics pipelines increasingly depend on immediate and reliable data intake, yet malformed records can still propagate into downstream dashboards, models, and decision systems before delayed validation catches them. Existing work on data quality management, schema enforcement, and observability highlights the importance of early validation, but practical streaming architectures that combine real-time schema checking with automated quarantine remain limited. This article presents a metadata-driven data quality firewall for analytics pipelines that performs inline schema validation, classifies failures during ingestion, routes invalid records into structured quarantine streams, and supports replay-based recovery after correction. The results show that the proposed framework maintains high schema validation accuracy and quarantine precision while adding only moderate ingestion latency, and it also improves downstream error reduction, replay recovery success, and pipeline continuity under multiple data quality failure scenarios. The study shows that a streaming data quality firewall can provide a practical foundation for protecting analytics pipelines from live data contamination.