AI-Driven Fault-Tolerant ETL Pipelines for Enhanced Data Integration and Quality

Abstract

The reliability and fault tolerance of ETL (Extract, Transform, Load) pipelines are crucial for ensuring data integrity in corporate environments. Traditional ETL systems often rely on manual interventions to resolve data inconsistencies, leading to inefficiencies and increased operational costs. This study introduces an AI-driven framework to enhance ETL fault tolerance by automating data cleaning, standardization, and integration. Leveraging machine learning models, the framework minimizes human intervention, improves data quality, and scales across diverse data formats. Using real-world datasets, the proposed solution demonstrates its ability to enhance operational efficiency and reduce errors in corporate data pipelines. The findings highlight the framework's ability to strengthen fault tolerance, ensure data quality, and provide organizations with a competitive edge in managing complex data ecosystems.