What you'll learn

Complete Curriculum for a successful PySpark Developer

Complete Flow of Installation of PySpark

Introduction to Spark

Understand SparkSession

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 Ee

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 Strats, Driver Conf, Executor Conf etc

HDFS Commands (Will be added shortly)

Python Fundamentals (Will be added shortly)

Description

This is a complete PySpark Developer course for Data Eeers 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 Ee, 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 Strats, Driver Conf, Parallelism Configurations, Executor Conf etc

HDFS Commands (Will be added shortly)

Python Fundamentals (Will be added shortly)

More will be added

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 Eeers and Data Scientists.




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