Failure Modes in Large-Scale Data Pipelines: A Taxonomy and Systems Analysis

Large-scale data pipelines have become critical infrastructure for modern data-intensive applica-tions, processing petabytes of data across distributed computing environments. However, thesesystems face numerous failure modes that can compromise data integrity, system availability, andprocessing correctness. This paper presents a comprehensive taxonomy of failure modes in large-scale data pipelines and develops a systems analysis framework for understanding their charac-teristics, impacts, and mitigation strategies. Through systematic analysis of 30 highly relevantresearch papers spanning stream processing, batch processing, and hybrid architectures, we iden-tify seven primary failure mode categories: infrastructure failures, data quality issues, distributedsystem failures, processing failures, resource exhaustion, configuration errors, and cascading failures.We examine fault tolerance mechanisms including checkpointing strategies, lineage-based recovery,replication approaches, and predictive failure management. Our analysis reveals that modern sys-tems like Apache Flink, Spark Streaming, and Kafka Streams employ diverse recovery strategieswith distinct tradeoffs between runtime overhead and recovery efficiency. We provide detailed casestudies from production systems and propose a unified framework for evaluating fault toleranceapproaches. This work contributes to both theoretical understanding and practical guidance fordesigning resilient large-scale data processing systems.