Full Course - Spark / PySpark For Industry | Hindi - Spark RDD Deep Dive Architecture Every Data Engineer Must Know

Many data engineering candidates know how to use Spark APIs but struggle to explain what actually happens underneath, especially when interviewers go deep into RDD concepts. In this video, we provide a long format and structured explanation of the complete Spark RDD stack from an interviewer’s perspective. You will learn what RDDs really are, how they fit into the Spark architecture, how execution flows from RDD creation to task execution, and what level of depth interviewers expect when discussing RDDs. This session is designed for beginners, data analysts, and career switchers who want to master Spark fundamentals before moving to DataFrames, optimizations, and production pipelines on platforms like Databricks, AWS Glue, and Azure Data Factory. What’s Covered: * RDD Introduction & Features * Creating RDDs from:  Python Variables  Local File System  Amazon S3  HDFS * Default Parallelism & Minimum Partitions * Repartitioning RDDs * Lazy Evaluation & Transformations * Shuffling Explained * How RDD Stores Data – Debunking Common Myths! Creating RDD's PySpark Github Exercises Link: https://github.com/tg117official/pyspark200/tree/master/01_rdd_operations/01_creating_rdds What does the Data Engineering ecosystem consists of..? -- Azure, DataBricks, Airflow, AWS EMR, Glue, Lambda, Kinesis, Spark-Streaming, Python Programming, Apache Hadoop, Apache Hive, PySpark, SparkSQL, Kafka, etc.. FullStack Courses and projects : www.tg117.in Request a Call Back from Us to know about the full stack courses in Data Engg and career support Google Forms- https://forms.gle/BFRskqhD3GVNSV5U6 Detailed syllabus of Data Engineering with AWS, Azure DataBricks, Apache Spark 3, Kafka, Hive, Hadoop, Airflow : link : https://versiontwo.tg117.in/courses/live-data-engineering-with-aws-azure-databricks-apache-spark-3-kafka-hive-hadoop-airflow/ 0:00 - Intro slides & topics covered in previous video 0:53 - Spark architecture all elements stack and their flow. 01:43 - Why RDD is the foundation to know about Data Frame. 03:40 - RDD arrays, Partitions "getNumPartitions()", Distributed RDD explained 08:26 - Immutability : practical demonstration 12:35 - Common ways to create RDD and related examples 13:40 - Default parallelism "sc.defaultParallelism()" & Number of partitions explained 17:27 - Create RDD from csv textfiles with "sc.textfile()" 20:03 - AWS S3 csv file to create RDD, practical demonstration + partition logic 22:20 - HDFS partitions of a file explained 27:52 - RDD repartition(): how and when to use it change the number of partitions of a file. 28:56 - Lazy Evaluation | RDD operations | Transformations (flatMap, map, groupByKey) & Actions (collect, take) 37:10 - Narrow vs Wide Transformation ( No shuffle vs Shuffling ) 42:36 - Does RDD store data 45:49 - Summary of video topics #ApacheSpark #RDD #PySpark #BigData #SparkTutorial #DataEngineering #LazyEvaluation #Shuffling #SparkTransformations #Repartition #HDFS #S3 #RDDStorage