Learn PySpark in depth with hundreds of Practical examples. Be a complete PySpark Developer. Set up a Hadoop Cluster. What you'll learn Complete Curriculum for a successful PySpark Developer Hadoop Single Node Cluster Set up and Integrate with Spark 2.x and Spark 3.x Complete Flow of Installation of PySpark (Windows and Unix) Detailed HDFS Course Python Crash Course Introduction to Spark Understand SparkSession Spark RDD Fundamentals, Operations, Persistence. Practical Examples to solve problems. Spark Cluster Architecture - Execution, YARN, JVM Processes, DAG Scheduler, Task Scheduler Spark Shared Variables Spark SQL Architecture, Catalyst Optimizer, Volcano Iterator Model, Tungsten Execution Engine DataFrame Fundamentals DataFrame Rows, Columns and DataTypes. Practical examples. ETL Using DataFrame (Extraction APIs, Transformation APIs, and Loading APIs). Practical Examples. Optimization and Management - Join Strategies, Driver Conf, Executor Conf etc Description This is a complete PySpark Developer course for Data Engineers and Data Scientists and others who wants to process Big Data in an effective manner. We will cover below topics and more Complete Curriculum for a successful PySpark Developer Set up Hadoop Single Node Cluster and Integrate it with Spark 2.x and Spark 3.x Complete Flow of Installation of Standalone PySpark (Unix and Windows Operating System) Detailed HDFS Commands and Architecture. Python Crash Course Introduction to Spark (Why Spark was Developed, Spark Features, Spark Components) Understand SparkSession Spark RDD Fundamentals How to Create RDDs RDD Operations (Transformations & Actions) Spark Cluster Architecture - Execution, YARN, JVM Processes, DAG Scheduler, Task Scheduler RDD Persistence Spark Shared Variables - Broadcast Spark Shared Variables - Accumulators) Spark SQL Architecture, Catalyst Optimizer, Volcano Iterator Model, Tungsten Execution Engine, Different Benchmarks Difference between Catalyst Optimizer and Volcano Iterator Model Spark Commonly Used Functions - Version, range, createDataFrame, sql, table, SparkContext, conf, read, udf, newSession, stop, catalog etc DataFrame Built-in functions - new column functions, encryption functions, string functions, regexp functions, date functions, null functions, collection functions, na functions, math and statistics functions, explode functions, flatten functions, formatting and json functions What is Partition, What is Repartition What is Coalesce Repartition Vs Coalesce Extraction - csv file, text file, Parquet File, orc file, json file, avro file, hive, jdbc DataFrame Fundamentals What is a DataFrame DataFrame Sources DataFrame Features DataFrame Organization DataFrame Rows, DataFrame Columns DataTypes. Practical examples. Perform ETL Using DataFrame -- Extraction APIs --Transformation APIs -- Loading APIs -- Practical Examples. Optimization and Management - Join Strategies, Driver Conf, Parallelism Configurations, Executor Conf etc Who this course is for Any IT professional willing to learn advanced Big Data Technologies like PySpark. Python Developers who wants to learn Spark. Data Engineers and Data Scientists.