Jonathan M., Lee and Rachel A., Martinez (2024) CASE STUDY REDUCING ETL FAILURES THROUGH PREDICTIVE ML MODELS. Journal of Adaptive Learning Technologies, 1 (5). pp. 50-58. ISSN 2997-3902

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Abstract

Extract, Transform, Load (ETL) pipelines are the lifeblood of enterprise data ecosystems, yet they remain highly vulnerable to silent failures, schema drift, and performance bottlenecks. Traditional monitoring approaches—based on static thresholds and reactive alerts—struggle to keep pace with the scale and complexity of modern data operations. This case study explores how predictive machine learning (ML) models can be embedded into ETL workflows to proactively identify, diagnose, and reduce failures before they impact downstream analytics.

Item Type:	Article
Subjects:	Q Science > Q Science (General)
Divisions:	Postgraduate > Master's of Islamic Education
Depositing User:	Journal Editor
Date Deposited:	10 Sep 2025 08:48
Last Modified:	10 Sep 2025 08:48
URI:	http://eprints.umsida.ac.id/id/eprint/16337

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