Spark and AWS Glue Performance Tuning and Troubleshooting

Slow data pipelines and unexpected out-of-memory errors can stall your data engineering workflows and inflate cloud costs. This text-based course guides you through the mechanics of the Spark execution engine and AWS Glue to help you build highly optimized data pipelines. You will transition from basic pipeline configurations to confidently diagnosing bottlenecks and fine-tuning engine performance. What you'll learn: - Understand core Spark memory management, executor behaviors, and driver roles. - Diagnose Spark out-of-memory (OOM) errors by analyzing failure signatures in CloudWatch logs. - Configure AWS Glue worker scaling strategies, comparing horizontal scaling with vertical worker upgrades. - Optimize data layout using Snappy-compressed Parquet files and ideal file-sizing practices. - Apply partition pruning and modern data storage layouts to minimize data scanning and accelerate queries. This comprehensive text-only course begins with foundational concepts of distributed computing before moving into hands-on diagnostic scenarios and scaling strategies. Designed for data engineers, developers, and cloud practitioners, this course requires only a basic familiarity with data pipelines. Start reading today to master the art of data engine optimization.