Dynamic Partition Pruning: How It Works (And When It Doesn’t)

Dive deep into Dynamic Partition Pruning (DPP) in Apache Spark with this comprehensive tutorial. If you've already explored my previous video on partitioning, you're perfectly set up for this one. In this video, I explain the concept of static partition pruning and then transition into the more advanced and efficient technique of dynamic partition pruning. You'll learn through practical examples, starting with a listening activity dataset partitioned by date, and then move to a complex scenario involving a join operation between listening activity and songs datasets. The video meticulously explains how DPP optimizes query performance by reducing unnecessary data scans, and the conditions necessary for its effective implementation. I also highlight the differences between static and dynamic partition pruning and the importance of having partitioned data for DPP to work effectively. Whether you're a data engineering enthusiast or a professional working with Spark, this video will enhance your understanding of optimizing Spark queries using Dynamic Partition Pruning. Don't forget to like, share, and subscribe for more insightful content on Apache Spark and big data analytics! 📄 Complete Code on GitHub: https://github.com/afaqueahmad7117/spark-experiments/blob/main/spark/5_1_dynamic_partition_pruning.ipynb 🎥 Full Spark Performance Tuning Playlist: https://www.youtube.com/playlist?list=PLWAuYt0wgRcLCtWzUxNg4BjnYlCZNEVth 🔗 LinkedIn: https://www.linkedin.com/in/afaque-ahmad-5a5847129/ Chapters 00:00 Introduction 00:23 What is static pruning? 02:47 Dynamic partition pruning 12:07 Caveats when using dynamic partition pruning 14:29 Code to understand dynamic partition pruning 20:28 Thank you #spark #dataengineering #apachespark #partition #partitioning #dynamicpartitionpruning #staticpruning #pruning #sparkperformancetuning #sparkoptimization #bigdataanalytics #sparktutorial #dataoptimization #sparkinterviewquestions #dataengineering #interviewquestions #dataengineerinterviewquestions #azuredataengineer #dataanalystinterview