Performance Optimization in Apache Spark
Optimize Apache Spark workflows with advanced techniques. Learn partitioning, caching, join strategies, and adaptive query execution (AQE) to handle large datasets efficiently and improve query performance for real-world big data scenarios.
What you'll learn
Efficient performance optimization is critical for scaling Apache Spark workflows effectively.
In this course, Performance Optimization in Apache Spark, you’ll gain the ability to optimize Spark applications for handling large-scale data processing challenges.
First, you’ll explore partitioning strategies to distribute workloads efficiently and reduce data shuffling while learning techniques like wide and narrow transformations.
Next, you’ll discover how caching and persistence can improve iterative processing, along with effective join strategies such as broadcast joins and bucketing to enhance performance in large datasets.
Finally, you’ll learn to leverage adaptive query execution (AQE) features, including dynamic partition coalescing, dynamic join selection, and handling data skew to optimize complex queries seamlessly.
When you’re finished with this course, you’ll have the skills and knowledge of Apache Spark needed to create efficient, scalable workflows for real-world big data challenges.
Table of contents
About the author
Pinal Dave is an SQL Server Performance Tuning Expert and independent consultant with over 22 years of hands-on experience. He holds a Master of Science degree and numerous database certifications. Pinal has authored 14 SQL Server database books and 81 Pluralsight courses. To freely share his knowledge and help others build their expertise, Pinal has also written more than 5,800 database tech articles on his blog at https://blog.sqlauthority.com.