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Complete PySpark Developer Course

Learn PySpark in-depth with hundreds of Practical examples. Be a complete PySpark Developer.

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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

  • Complete Flow of Installation of PySpark

  • 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 and Accumulators)

  • Spark SQL Architecture, Catalyst Optimizer, Volcano Iterator Model, Tungsten Execution Engine, Different Benchmarks

  • Spark Commonly Used Functions - Version, range, createDataFrame, sql, table, SparkContext, conf, read, udf, newSession, stop, catalog etc

  • DataFrame Built-in functions - new column, encryption, string, regexp, date, null, collection, na, math and statistics, explode, flatten, formatting and json

  • What is Partition, Repartition and 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, Columns and DataTypes. Practical examples.

  • ETL Using DataFrame (Extraction APIs, Transformation APIs, and Loading APIs). Practical Examples.

  • Optimization and Management - Join Strategies, Driver Conf, Parallelism Configurations, Executor Conf etc

  • HDFS Commands (Will be added shortly)

  • Python Fundamentals (Will be added shortly)

  • More will be added

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