A predictive infrastructure monitoring model for data lakes using quality metrics and DevOps automation

Abstract

As data lakes become critical components of modern enterprise data architecture, their scale and complexity demand a shift from reactive to predictive infrastructure monitoring. Traditional approaches often fail to detect latent failures, configuration drift, or data quality degradation in time to prevent downstream disruptions. This paper proposes a conceptual model that integrates data quality metrics and DevOps automation to enable predictive monitoring in data lake environments. By treating metrics such as completeness, freshness, and consistency as early indicators of infrastructure instability, the model establishes a feedback-driven monitoring framework capable of anticipating failures and triggering automated remediation. The architecture is structured around four core layers: data ingestion, quality monitoring, predictive signal processing, and DevOps automation. Metric collection agents capture real-time indicators from data pipelines and logs, which are then normalized and analyzed for anomalies using statistical and machine learning techniques. These signals inform rules engines and dashboards, which initiate infrastructure-as-code playbooks for scaling, restarting, or adjusting compute environments. Integration with DevOps tools ensures automated responses' transparency, auditability, and consistency. The model enhances reliability, improves resource efficiency, and fosters cross-team collaboration by embedding observability into the data lifecycle. It also supports governance and continuous learning through metric traceability and post-mortem analysis. Finally, the paper discusses future directions, including AI-enhanced anomaly detection, benchmarking against traditional systems, and integration with AIOps ecosystems to expand predictive capabilities in cloud-native data operations.