Challenges and Solutions for Managing Errors in Distributed Batch Processing Systems and Data Pipelines
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V4I4P107Keywords:
Distributed systems, batch processing, data pipelines, error handling, fault tolerance, data quality, schema evolutionAbstract
Data pipelines and distributed batch processing are the core of the contemporary data infrastructure, as they allow businesses to handle large-scale data, which is heterogeneous in nature, efficiently across multiple sources. Nevertheless, there exist abundant error management issues around such systems owing to their distributed, scale, and complex nature. Data inconsistencies, schema evolution, transient failures, resource contention and a lack of observability are among common problems that can lead to a compromise of data quality, processing reliability, and operational uptime. Provided below is an in-depth analysis of these challenges, differentiating the types of errors, how they occur, and the limitations of the current mechanisms to mitigate such errors, like fixed retries, dead-letter queues, and manual recovery workflows. Based on empirical assessments and case studies of the industry, we present the idea of the next-gen error management system that would integrate intelligent error classification, programmable retry rules, data lineage tracing, and observability improvements. Experimental verification in a multiplicity of settings, such as e-commerce, the internet of things, and financial systems, has proven to lead to a considerable decrease in failure rates, recovery time, and false positives, at the rate to baseline strategies. These papers also discuss future initiatives that include self-healing pipelines supported by AI, distributed transactions, and validation mechanisms, aiming to provide stronger data governance and management. The paper provides valuable real-life lessons to data engineers, architecture, and platform teams to create fault-tolerant, scalable autonomous batch workload processing systems that can satisfy the needs of contemporary data-driven companies
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