Complete PySpark Developer Course (Spark with Python)




Complete PySpark Developer Course (Spark with Python)

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


Learn PySpark in depth with hundreds of Practical examples. Be a complete PySpark Developer. Set up a Hadoop Cluster.

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What you will 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)

Rating: 4.26613

Level: All Levels

Duration: 30.5 hours

Instructor: Sibaram Kumar


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