Master Reading Spark DAGs

Spark Performance Tuning In this tutorial, we dive deep into the core of Apache Spark performance tuning by exploring the Spark DAGs (Directed Acyclic Graph). We cover the Spark DAGs (Directed Acyclic Graph) for a range of operations from reading files, Spark narrow and wide transformations with examples, aggregation using groupBy count, groupBy count distinct. Understand the differences between sort merge and broadcast joins, and analyze the DAG from different perspectives with practical examples. This video is a treasure trove for both beginners and experienced Spark users looking to optimize their code and understand the inner workings of Apache Spark. We examine the DAG, input batches, and partitions in great detail, understand the significance of metadata, and explore how Spark optimizes the execution of jobs and stages. 📄 Complete Code on GitHub: https://github.com/afaqueahmad7117/spark-experiments/blob/main/spark/3_reading_query_DAGs.ipynb 🎥 Full Spark Performance Tuning Playlist: https://www.youtube.com/playlist?list=PLWAuYt0wgRcLCtWzUxNg4BjnYlCZNEVth 🎥 Link to Spark Query Plan Video: https://www.youtube.com/watch?v=KnUXztKueMU&t=2049s 🔗 LinkedIn: https://www.linkedin.com/in/afaque-ahmad-5a5847129 Chapters: 00:00 Introduction 00:34 Module imports 00:51 Topics covered 01:54 Spark DAG for Reading a file 07:36 DAG for Narrow transformations 11:17 Wide transformations introduction 11:24 DAG for Sort Merge join (wide transformation) 18:30 DAG for Broadcast join (narrow transformation) 20:15 DAG for Aggregations Group by count (wide transformation) 24:41 DAG for Aggregations Group by sum (wide transformation) 25:44 DAG for Aggregations Group by count distinct (wide transformation) #ApacheSpark #SparkPerformanceTuning #DataEngineering #SparkDAG #SparkOptimization #dataengineering #interviewquestions #azuredataengineer