Real-Time ETL Optimization Using Adaptive Machine Learning Models

  • Authors

    • Dr. Noorul Hasan Professor, University of Dhaka, Bangladesh. Author

    DOI:

    https://doi.org/10.67228/30715717/IJDEIC-2021PII9M6H

    Published 12-20-2021

  • Real-time ETL, Adaptive Machine Learning, Online Learning, Data Pipeline Optimization, Stream Processing, Concept Drift, Dynamic Resource Allocation, Big Data, Workflow Scheduling, Intelligent ETL

    Issue

    Vol. 4 No. 2 (2021)

    Section

    Articles

    How to Cite

    [1]
    N. Hasan, “Real-Time ETL Optimization Using Adaptive Machine Learning Models”, IJDEIC, vol. 4, no. 2, pp. 01–12, Dec. 2021, doi: 10.67228/30715717/IJDEIC-2021PII9M6H.

  • Abstract

    In an era where real-time data processing is pivotal to decision-making, optimizing ETL (Extract, Transform, Load) pipelines for performance and efficiency is more critical than ever. This paper proposes a novel approach to real-time ETL optimization using adaptive machine learning models. Unlike traditional static optimization techniques, adaptive models continuously learn from streaming data to adjust ETL operations dynamically, ensuring minimal latency, efficient resource utilization, and improved data throughput. We present a modular architecture that integrates online learning algorithms into each phase of the ETL process, enabling real-time responsiveness to evolving data patterns and system conditions. Experimental results demonstrate substantial improvements over conventional ETL strategies, particularly in environments characterized by high data velocity and volume. This research lays the groundwork for next-generation, intelligent data pipelines capable of self-optimization in real-time settings.

  • References

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