PySpark Project- End to End Real Time Project Implementation

Name: PySpark Project- End to End Real Time Project Implementation
Rating: 4.1 (500 reviews)

Implement PySpark Real Time Project. Learn Spark Coding Framework. Transform yourself into Experienced PySpark Developer

Last updated 10/2025

English

What you'll learn

End to End PySpark Real Time Project Implementation.
Projects uses all the latest technologies - Spark, Python, PyCharm, HDFS, YARN, Google Cloud, AWS, Azure, Hive, PostgreSQL
Learn a pyspark coding framework, how to structure the code following industry standard best practices.
Install a single Node Cluster at Google Cloud and integrate the cluster with Spark.
install Spark as a Standalone in Windows.
Integrate Spark with a Pycharm IDE.
Includes a Detailed HDFS Course.
Includes a Python Crash Course.
Understand the business Model and project flow of a USA Healthcare project.
Create a data pipeline starting with data ingestion, data preprocessing, data transform, data storage ,data persist and finally data transfer.
Learn how to add a Robust Logging configuration in PySpark Project.
Learn how to add an error handling mechanism in PySpark Project.
Learn how to transfer files to S3 and Azure Blobs.
Learn how to persist data in Hive and PostgreSQL for future use and audit (Will be added shortly)

27 sections • 154 lectures • 14h 49m total length

Introduction and Installation Flow Chart6:02
Resources0:01
Register Free at Google Cloud (GCP) and Launch an Ubuntu based Virtual Machine7:27
Set Up Python and Java4:42
Set up Secure Connect to Localhost3:03
Set up Hadoop tar, HDFS, YARN and manage Cluster Services16:16
Set Up Docker, PostgreSQL, Hive Part 115:29
Set up Docker, PostgrSQL, Hive, Metastore Part 26:26
Set up Spark 2.x and Spark 3.x Part 116:30
Set up Spark 2.x and Spark 3.x Part 210:57
Set up Web UI and ports for Cluster and Application History12:30
Manage the Cluster - Start & Stop the Cluster2:21

What is HDFS and Why to use HDFS3:38
Resources0:01
HDFS Components and Metadata3:54
Data Blocks and Replication5:07
Rack Awareness1:49
HDFS Read Mechanism Architecture2:35
Exercise - HDFS CLI Help Commands2:14
Exercise - Bring Data from GitHub to Local to HDFS2:32
Exercise - Listing and Sorting Files and Directories in HDFS2:54
Explore listing and sorting in HDFS using the list command, learn help usage for documentation, apply pattern filters, enable recursive views, and tailor output to show only directories or files.
Exercise - Create or Remove Directories in HDFS9:04
Exercise - Copy Data from HDFS to Local5:08
Exercise - Copy data from Local to HDFS6:54
Exercise - Preview Data in HDFS3:51
Exercise - Knowing Statistics in HDFS3:42
Exercise - Knowing Storage in HDFS File System3:07
Exercise - Metadata in HDFS4:54
Exercise - Managing File Permissions in HDFS5:28
Exercise - Update Properties in HDFS8:19
Note0:08

Project Folders Set up at PyCharm4:04
Project Integration with PySpark2:34
Connect Python with PySpark by configuring content roots and Py4J, then validate integration by creating and printing a Spark session object.
Understand input and output File Layouts5:25
Move Input Files to Project Staging Folder0:31
Create two sub-folders under staging path: Dimension City and fact. Move the input city file to Dimension City and the input prescriber file to the fact folder.

Basic Knowledge on PySpark. You may brush up your knowledge from my another course 'Complete PySpark Developer Course".
Basic Knowledge on HDFS (A detailed HDFS course is included in this course)
Basic Knowledge on Python (A Python Crash course is included in this course)

End to End PySpark Real Time Project Implementation.
Projects uses all the latest technologies - Spark, Python, PyCharm, HDFS, YARN, Google Cloud, AWS, Azure, Hive, PostgreSQL.
Learn a pyspark coding framework, how to structure the code following industry standard best practices.
Install a single Node Cluster at Google Cloud and integrate the cluster with Spark.
install Spark as a Standalone in Windows.
Integrate Spark with a Pycharm IDE.
Includes a Detailed HDFS Course.
Includes a Python Crash Course.
Understand the business Model and project flow of a USA Healthcare project.
Create a data pipeline starting with data ingestion, data preprocessing, data transform, data storage ,data persist and finally data transfer.
Learn how to add a Robust Logging configuration in PySpark Project.
Learn how to add an error handling mechanism in PySpark Project.
Learn how to transfer files to AWS S3.
Learn how to transfer files to Azure Blobs.
This project is developed in such a way that it can be run automated.
Learn how to add an error handling mechanism in PySpark Project.
Learn how to persist data in Apache Hive for future use and audit.
Learn how to persist data in PostgreSQL for future use and audit.
Full Integration Test.
Unit Test.

Any IT professional willing to learn how to Implement a real time PySpark Project.
Data Engineers and Data Scientists.