Complete PySpark Developer Course (Spark with Python)
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
- Python Fundamentals and HDFS Commands. This course includes a section on detailed HDFS commands and a section on Python as well. So anyone can start this course with out any prior knowledge..
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)
Spark RDD Fundamentals
How to Create RDDs
RDD Operations (Transformations & Actions)
Spark Cluster Architecture - Execution, YARN, JVM Processes, DAG Scheduler, Task Scheduler
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
What is a DataFrame
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.
CCA Spark and Hadoop Developer Certified.
I have over 13 years of Professional Experience in Data Engineering Functions: Data Warehousing, Data Analytics, Data Models, Reporting, Database Design, Modelling and Development. I am a certified CCA Spark and Hadoop Developer, AWS and Oracle SQL, PLSQL. I enjoy teaching students in different technologies such as PySpark, Python, AWS and Oracle.