Spark Performance Tuning Essentials

This scenario-based learning module focuses on Spark and Delta Lake performance optimization concepts through short, thought-provoking questions. Instead of hands-on exercises, you’ll analyze real-world
scenarios that reflect common performance challenges in production data pipelines. The goal is to help you develop the analytical mindset and reasoning skills needed to diagnose and resolve performance
issues effectively.

Through these scenario questions, you’ll strengthen your understanding of key Spark optimization areas:

• Memory management — understanding driver/executor configurations, caching vs persistence, and detecting data skew.
• Schema design — avoiding inferSchema overhead, defining data types explicitly, and managing schema evolution in Delta tables.
• Data storage — choosing optimal file formats, applying effective partitioning, and leveraging compression for efficiency.
• Shuffle and join optimization — interpreting repartitio this is for your reference..Please give me in this format