Evaluating the Performance of SQL-Based vs. Python-Based Data Processing in Cloud Computing for Machine Learning Applications

Abstract

In cloud computing environments, efficient data processing is essential for machine learning applications, where the choice of processing tools directly impacts performance and scalability. This study compares SQL-based and Python-based data processing to evaluate their effectiveness in handling large datasets and supporting machine learning workflows. Through experiments on AWS using Amazon Redshift for SQL and Pandas/Dask for Python, we analyzed processing speed, memory utilization, scalability, and integration complexity across different tasks. Results indicate that SQL outperforms Python in speed and memory efficiency for simple, structured data transformations, making it ideal for large-scale data cleaning and aggregation tasks. However, Python offers greater flexibility and seamless integration with machine learning frameworks, proving advantageous for complex transformations and feature engineering. Statistical analyses confirm SQL’s strength in handling high-volume structured data, while Python is better suited for tasks requiring intricate preprocessing and machine learning model integration. These findings suggest that a hybrid approach can combine the strengths of both SQL and Python for optimal data processing in cloud-based machine learning workflows.