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
