Automated Root Cause Analysis in Distributed Cloud Environments: An Unsupervised AIOps Approach Using BigQuery ML

Abstract

Modern distributed cloud environments present unprecedented challenges for operational monitoring and incident management, where traditional rule-based systems fail to effectively handle the scale and complexity of containerized microservices architectures. This article presents an advanced AIOps framework that leverages unsupervised machine learning techniques through BigQuery ML to automate anomaly detection and root cause analysis in distributed cloud systems. The framework aggregates heterogeneous telemetry data from container orchestration platforms, application traces, and network flow logs into unified analytics pipelines that establish dynamic operational baselines without requiring labeled training datasets. The implementation employs clustering algorithms and statistical deviation detection mechanisms to identify anomalous patterns across multiple system layers while suppressing operational noise and false positive alerts. Experimental evaluation in production environments demonstrates substantial improvements in detection accuracy, significant reductions in mean time to detection and resolution, and enhanced operational efficiency for site reliability engineering teams. The SQL-based machine learning implementation enables operations teams to deploy sophisticated analytics without specialized data science expertise, providing scalable anomaly detection capabilities that adapt to evolving operational patterns in dynamic cloud environments.