Cloud ETL optimization with AWS glue and spark

Cloud-native ETL has become a cornerstone of modern data architectures, enabling real-time analytics, scalable machine learning pipelines, and cost-efficient data processing. AWS Glue and Apache Spark represent a powerful duo for building robust and serverless ETL frameworks. This review has examined their capabilities in depth—covering architecture, tuning methods, and best practices. It also highlights experimental benchmarks, key optimization strategies, and emerging trends that define the future of ETL. The findings suggest that with the right design patterns and tuning, organizations can significantly boost performance while reducing both cost and operational complexity.