Udemy – Spark Performance Tuning for Data Engineers: Part2 – Spill

Unlock the true potential of Apache Spark by mastering storage-related performance tuning techniques. This hands-on course is packed with real-world scenarios, guided demos, and practical use cases that will help you fine-tune Spark storage strategies for speed, efficiency, and scalability. This course is perfect for Intermediate Data Engineers & Spark Developers as well as Aspiring Achitects who wants to optimize Spark jobs, reduce resource costs, and ensure fast, reliable performance for large-scale data applications.

What You’ll Learn:

• 1. Understand how Apache Spark handles storage internally: memory vs disk
• 2. Learn when and how to use Spark caching and persistence effectively
• 3. Compare and choose the right storage levels: MEMORY_ONLY, MEMORY_AND_DISK, etc.
• 4. Use real-world examples and hands-on demos to benchmark storage decisions
• 5. Learn how to monitor storage metrics using the Spark UI
• 6. Handle memory spills, disk I/O bottlenecks, and storage tuning in cluster environments
• 7. Apply best practices for storage optimization in cloud and on-prem Spark clusters

Why Take This Course:

• 100% Hands-on: Focused on practical implementation, not just theory
• Designed for Data Engineers, Spark Developers, and Big Data Practitioners
• Covers both foundational concepts and advanced tuning techniques
• Teaches how to measure performance gains using real metrics
• Helps you make cost-efficient decisions for big data storage

Tools & Technologies Covered:

• Apache Spark (2.x and 3.x)
• DataBricks
• Spark UI
• HDFS, DataLake (for storage scenarios)