Grow your career w/ Educative (55% off): Additional 10% off: Please note I may earn a small commission for any purchase through these links - Thank you for supporting the channel! :) ____________________________________________ Spark still feels like black magic? These Apache Spark concepts will change that. In this video, I break down 30 core Apache Spark concepts that helped me crack big-tech interviews and finally understand Spark under the hood—so your jobs run faster! What you’ll learn: • Why Spark beats MapReduce with in-memory + DAG execution • Transformations vs actions, shuffles, partitions, and the Spark DAG • Spark architecture; How jobs, stages, tasks, driver/executors/YARN work • Spark memory (on-heap, off-heap, unified) for tuning If Spark has ever felt confusing, this is your mental model. Watch till the end. Subscribe for more videos on Spark, Data Engineering deep dives & Interview preparation! Chapters 0:00 Why these 30 Apache Spark concepts matter for interviews & real jobs 0:29 Why MapReduce is slow — the core performance bottleneck 0:51 MapReduce word-count example (Map step) 1:52 MapReduce shuffle/sort + Reduce step (disk I/O pain) 2:53 MapReduce limitations recap — disk writes + rigid 2-step model 3:23 How Spark fixed MapReduce — in-memory processing 4:13 Spark’s DAG model — transformations, actions, jobs, stages, tasks 5:06 Sponsor break: Educative 6:09 Detailed Spark architecture - driver, executors, cluster manager 15:39 Deployment modes — Cluster mode vs Client mode 17:04 Transformations vs Actions — lazy execution made simple 19:18 Narrow vs Wide transformations — when shuffles happen 22:30 Spark plans — resolved, unresolved, logical, physical 27:06 Jobs, stages, tasks with a real Spark code example 35:39 Spark executor memory deep dive begins 40:07 Memory sizing example — & fractions 41:49 Final recap of the 30 concepts Connect w/ me here: LinkedIn: YouTube Channel: @afaqueahmad7117 Playlists: Interview Preparation: Spark Performance Tuning: Data Engineering Roadmap: How I Mastered Data Modeling: Cracking Interviews @ Apple, Uber, Atlassian, Databricks: Github: Spark Performance Tuning Codes: #databricks #databrickstutorial #deltalake #dataengineering #bigdata #ApacheSpark #DataEngineering #SparkPerformance