Systematic approach to root cause analysis in distributed data processing systems

Distributed data processing is a powerful capability, but with it comes the challenge of ensuring the reliability and performance of the system often on a larger scale, it is especially important to systematically identify the root cause of failures and address them accordingly. Cloud computing has changed the game by introducing scale, flexibility and low-cost alternatives to big data processing. With distributed systems getting increasingly complex, diagnosing failures has become defeated due to many components relying on each other and as workloads change dynamically. This paper presents a systematic approach for performing root cause analysis (RCA) in a distributed setting one that covers automatic monitoring, anomaly detection, and log-based analytics. Overcoming the RCA challenges with cloud-native tools like Azure Data Factory, Power BI, and anomaly detection through machine learning are discussed. The research also discusses best practices for reducing downtime and performance optimization with predictive maintenance strategy. Cloud technologies have enabled organizations to achieve greater operational efficiency through better system resilience and decision-making in modern data-driven environment.