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Fix Data Bottlenecks: Optimize Spark Performance

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Fix Data Bottlenecks: Optimize Spark Performance

This course is part of multiple programs.

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2 hours to complete
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Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

2 hours to complete
Flexible schedule
Learn at your own pace

What you'll learn

  • Performance bottlenecks in distributed systems often stem from uneven data distribution rather than insufficient computational resources.

  • Visual execution plan analysis is essential for identifying specific stages where data processing imbalances occur.

  • Proactive partition strategy selection prevents performance degradation more effectively than reactive optimization

  • Spark's shuffle.partitions configuration and broadcast join patterns are fundamental tools for sustainable pipeline optimization.

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Recently updated!

February 2026

Assessments

4 assignments¹

AI Graded see disclaimer
Taught in English

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There are 2 modules in this course

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

Learners will develop foundational skills for analyzing distributed execution plans to identify performance bottlenecks caused by data shuffle and skew patterns in Spark applications.

What's included

3 videos3 readings1 assignment1 ungraded lab

3 videos•Total 14 minutes
  • Why Performance Analysis Saves Data Teams from Pipeline Disasters•3 minutes
  • Understanding Spark's Distributed Execution Architecture•6 minutes
  • Interpreting Visual Execution Metrics and Performance Indicators•6 minutes
3 readings•Total 22 minutes
  • Data Shuffle and Skew: The Hidden Performance Killers•8 minutes
  • Navigating Spark's Execution Monitoring Interface•7 minutes
  • Identifying Bottleneck Patterns in Task Execution Metrics•7 minutes
1 assignment•Total 3 minutes
  • Knowledge Check: Execution Plan Analysis Fundamental•3 minutes
1 ungraded lab•Total 20 minutes
  • Diagnose Performance Bottlenecks Through Execution Plan Analysis•20 minutes

Learners will apply advanced optimization strategies to resolve identified performance bottlenecks through partition tuning, broadcast joins, and configuration optimization techniques.

What's included

1 video1 reading3 assignments

1 video•Total 7 minutes
  • Configuration Optimization: Tuning Spark for Maximum Performance•7 minutes
1 reading•Total 10 minutes
  • Partition Strategies and Broadcast Join Optimization Techniques•10 minutes
3 assignments•Total 30 minutes
  • Final Assessment: Comprehensive Performance Bottleneck Analysis and Resolution•12 minutes
  • Optimize Real-World Performance Scenario•15 minutes
  • Knowledge Check: Performance Optimization Strategies•3 minutes

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¹ Some assignments in this course are AI-graded. For these assignments, your data will be used in accordance with Coursera's Privacy Notice.