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)