Fix Data Bottlenecks: Optimize Spark Performance

Fix Data Bottlenecks: Optimize Spark Performance Did you know that inefficient data shuffling can slow Spark jobs by over 70%? Understanding how to detect and fix these bottlenecks is essential for achieving peak performance in distributed data systems. This Short Course was created to help professionals in this field optimize data pipeline performance and eliminate processing bottlenecks in distributed Spark environments. By completing this course, you will be able to analyze Spark execution plans, identify causes of data skew and shuffle inefficiencies, and apply optimization strategies—skills that improve processing speed, scalability, and overall data workflow efficiency. By the end of this 3-hour long course, you will be able to: Analyze distributed execution plans to resolve performance bottlenecks caused by data shuffle and skew. This course is unique because it blends practical Spark debugging with real-world optimization techniques, giving you hands-on experience in diagnosing distributed performance issues and fine-tuning large-scale data operations. To be successful in this project, you should have: Basic Spark concepts SQL fundamentals Understanding of distributed computing principles Data processing experience

• Analyze distributed execution plans to solve performance bottlenecks
• Identify causes of data skew and processing imbalance
• Apply partitioning strategies to enhance performance
• Use Spark configurations for sustainable pipeline optimization

• Basic computer and internet skills
• Ability to read course instructions in English
• Complete short practice activities