Unlock the performance potential of your Apache Spark applications! This course transforms beginners into confident Spark performance optimizers who can dramatically improve job execution times and resource efficiency.

This course is a direct response to industry demand, designed for the data engineer who is tired of reactive firefighting and ready to build proactively optimized, scalable systems. This Short Course was created to help data management and engineering professionals accomplish systematic Spark job optimization through strategic analysis of partitioning and caching patterns. By completing this course, you'll be able to inspect query execution plans in Spark UI, implement strategic partitioning keys that minimize data shuffling, persist intermediate DataFrames with appropriate storage levels, and validate performance improvements that you can apply immediately in your workplace. By the end of this course, you will be able to: Analyze partitioning and caching strategies to optimize Spark job performance This course is unique because it combines hands-on analysis using real Spark UI inspection with practical implementation techniques that deliver measurable performance gains – often 30% or more runtime improvements. To be successful in this project, you should have a background in basic Apache Spark concepts and data processing fundamentals.