Complete PySpark Developer Course (Spark with Python)

Genre: eLearning | MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 7.15 GB | Duration: 29h 1m

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.

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