Implementing Machine Learning for ETL Data Transformation and Anomaly Detection

The ETL (Extract, Transform, and Load) process is a critical data processing component. Traditional ETL processes lack the required capabilities and agility and fall short of coping with the dynamic and evolving nature of data ecosystems. The traditional ETL system presents a myriad of challenges to the data management process such as inefficiency in handling high-volume, high-velocity data, schema mapping, and preserving data quality. The purpose of the current study was to implement machine learning (ML) for ETL workflows by highlighting the role of ML in improving data transformation and anomaly detection, exploring methods for integrating ML in ETL pipelines, and analyzing the impact of ML in ETL pipelines through both practical and theoretical lenses. The credit card fraud dataset, comprising of 284,807 rows and 31 columns, was downloaded from Kaggle. The most significant problem with this dataset was the huge class imbalance. The dataset was balanced using a modern approach known as Synthetic Minority Over-sampling Technique (SMOTE). The Isolation Forest (IF) was used to detect anomalies in the dataset. The findings showed that implementing ML in ETL pipelines solves the problem of feature scale disparity, improving the balance and accuracy of the model. The project highlights the benefits of modern machine learning-driven ETL transformation and anomaly detection processes over traditional workflows.