Master Pyspark For Data Engineering
Published 4/2026
Created by Akkem Sreenivasulu
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Expert | Genre: eLearning | Language: English | Duration: 7 Lectures ( 1h 20m ) | Size: 828 MB
Master PySpark for Data Engineering (AWS, Azure, GCP, Snowflake)
What you’ll learn
✓ Master PySpark fundamentals to advanced concepts
✓ Understand distributed data processing and Spark architecture
✓ Build real-time and batch ETL pipelines using PySpark
✓ Perform data transformations using DataFrames and Spark SQL
✓ Work with large-scale datasets efficiently using Big Data techniques
✓ Implement data ingestion, transformation, and loading (ETL/ELT) workflows
✓ Design and build end-to-end data engineering pipelines
✓ Optimize Spark jobs using partitioning, caching, and performance tuning
✓ Handle real-world datasets and industry scenarios
✓ Work with structured and semi-structured data (JSON, Parquet, CSV)
✓ Understand data pipeline orchestration concepts
✓ Prepare for Data Engineering interviews with practical knowledge
Requirements
● Basic knowledge of Python programming (variables, loops, functions)
● Basic understanding of SQL (SELECT, WHERE, simple queries) – helpful but not mandatory
● Familiarity with data concepts (tables, rows, columns) is a plus
● A laptop/desktop with Windows, Mac, or Linux to install PySpark
● Willingness to learn Big Data and Data Engineering concepts
Description
PySpark for Data Engineering | AWS, Azure, GCP & Snowflake
Are you ready to become a job-ready Data Engineer by mastering PySpark and real-world data pipelines across multi-cloud platforms?
This course is designed to take you from fundamentals to advanced concepts in PySpark, while building end-to-end data engineering solutions using AWS, Azure, GCP, and Snowflake – exactly what companies expect in real projects.
What You Will Learn
• Master PySpark from basics to advanced
• Build real-time and batch data pipelines
• Work with large-scale distributed data processing
• Perform ETL (Extract, Transform, Load) using PySpark
• Integrate PySpark with
• Amazon Web Services (AWS Glue, S3, EMR)
• Microsoft Azure (Data Factory, Databricks)
• Google Cloud Platform (Dataproc, BigQuery)
• Snowflake (Cloud Data Warehouse)
• Optimize Spark jobs for performance and scalability
• Work with real-world datasets and scenarios
Real-Time Projects Included
This course is not just theory – you will build industry-level projects, such as
• End-to-end ETL pipeline using PySpark + AWS Glue
• Data ingestion pipeline with Azure Data Factory + Databricks
• Batch & streaming pipeline using GCP Dataproc
• Data warehousing solution using Snowflake
Why This Course is Different
• Covers Multi-Cloud Data Engineering (AWS + Azure + GCP)
• Focus on real-time industry use cases
• Designed for job-oriented learning
• Step-by-step explanation with hands-on practice
• Covers performance tuning & optimization
Who this course is for
■ Aspiring Data Engineers who want to build a strong career in Big Data
■ Python developers looking to transition into Data Engineering with PySpark
■ ETL developers who want to upgrade their skills to modern data pipelines
■ Professionals working with data who want to learn distributed data processing
■ Beginners who want to start their journey in Big Data and PySpark
■ Developers preparing for Data Engineering interviews
■ Anyone interested in working with large-scale data processing systems
■ Engineers who want to gain hands-on experience with: Amazon Web Services, Microsoft Azure, Google Cloud Platform,Snowflake
rapidgator.net/file/c20891150ad422bcb1a22d6ee021c37e/Master_PySpark_for_Data_Engineering.rar.html
