Self-Healing Data Pipelines for Fault-Tolerant Data Processing

  • Authors

    • Narendra Karmarkar Mathematician and Computer Scientist, Tata Institute of Fundamental Research, India. Author

    DOI:

    https://doi.org/10.67228/30715717/IJDEIC-2025PI4R9A

    Published 06-05-2025

  • Self-Healing Data Pipelines, Fault-Tolerant Systems, Distributed Data Processing, Autonomous Recovery, Machine Learning, Intelligent Monitoring, Data Engineering, Workflow Orchestration, Predictive Maintenance, Distributed Computing, Stream Processing, Cloud-Native Architectures

    Issue

    Vol. 8 No. 1 (2025)

    Section

    Articles

    How to Cite

    [1]
    N. Karmarkar, “Self-Healing Data Pipelines for Fault-Tolerant Data Processing”, IJDEIC, vol. 8, no. 1, pp. 01–17, Jun. 2025, doi: 10.67228/30715717/IJDEIC-2025PI4R9A.

  • Abstract

    Modern enterprise data ecosystems process information from diverse sources such as cloud applications, IoT devices, distributed databases, and social media. Traditional data pipelines often depend on manual fault handling and static recovery methods, leading to downtime, data inconsistency, and operational inefficiencies. Self-healing data pipelines address these challenges through autonomous fault detection, diagnosis, and recovery using AI, machine learning, anomaly detection, workflow orchestration, and predictive analytics. Techniques such as automated retry, dynamic rerouting, checkpoint recovery, adaptive scheduling, and intelligent resource allocation improve system resilience and fault tolerance. Technologies including Apache Kafka, Apache Spark, Apache Flink, and Kubernetes support scalable and autonomous pipeline management. This study proposes a framework for anomaly detection, root cause analysis, automatic remediation, and adaptive optimization in distributed data systems. Experimental results show improved availability, throughput, recovery efficiency, and reduced Mean Time to Recovery (MTTR), demonstrating that AI-driven self-healing pipelines significantly enhance scalability, resilience, and operational continuity in enterprise analytics infrastructures.

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