Pyspark For Data Engineers : Using Pyspark, Spark Sql, Parqu
 

Learn PySpark, Spark SQL, Docker, Streaming, Parquet, Performance Tuning, and Build a Real Retail Analytics Pipeline
What you'll learn
Understand Apache Spark architecture and distributed processing fundamentals
Learn how Spark executes jobs, stages, tasks, and DAGs
Work with PySpark DataFrames and Spark SQL efficiently
Read and write CSV, JSON, and Parquet datasets
Handle real-world messy datasets using schema enforcement
Perform transformations, aggregations, joins, and window functions
Understand lazy evaluation and Spark execution behavior
Optimize Spark jobs using partitioning and performance tuning techniques
Learn shuffle operations, repartitioning, and coalesce strategies
Build scalable and production-ready PySpark pipelines
Understand Spark streaming concepts and structured streaming
Implement Medallion Architecture using Bronze, Silver, and Gold layers
Process and transform large-scale retail transaction datasets
Build analytics-ready datasets optimized for BI tools like Power BI
Learn real-world data engineering workflows used in industry
Requirements
Basic Python knowledge is recommended
No prior PySpark experience is required
Willingness to learn big data and distributed systems
Description
Modern data engineering is built on distributed data processing, and PySpark has become one of the most important technologies for handling large-scale data pipelines in production environments.
This course is designed to take you from PySpark fundamentals to advanced data engineering concepts using practical, real-world examples and a complete end-to-end retail analytics project.
Unlike basic Spark tutorials that only teach syntax, this course focuses on how real data engineers work with Apache Spark in production systems. You will understand not only how to write PySpark code, but also why Spark behaves the way it does internally.
The course begins with Spark foundations and gradually moves into distributed processing, Spark architecture, transformations, schema management, optimization techniques, analytics functions, data storage strategies, streaming, and production-style engineering workflows.
You will also build a complete retail analytics data engineering pipeline using PySpark and Docker, where you will process over 1 million records using Medallion Architecture (Bronze, Silver, and Gold layers).
This course is highly practical and focused on industry-ready skills that are valuable for Data Engineering, Analytics Engineering, Big Data Engineering, and PySpark-focused roles.
What You Will Learn
• Understand Apache Spark architecture and distributed processing fundamentals
• Learn how Spark executes jobs, stages, tasks, and DAGs
• Work with PySpark DataFrames and Spark SQL efficiently
• Read and write CSV, JSON, and Parquet datasets
• Handle real-world messy datasets using schema enforcement
• Perform transformations, aggregations, joins, and window functions
• Understand lazy evaluation and Spark execution behavior
• Optimize Spark jobs using partitioning and performance tuning techniques
• Learn shuffle operations, repartitioning, and coalesce strategies
• Build scalable and production-ready PySpark pipelines
• Understand Spark streaming concepts and structured streaming
• Implement Medallion Architecture using Bronze, Silver, and Gold layers
• Process and transform large-scale retail transaction datasets
• Build analytics-ready datasets optimized for BI tools like Power BI
• Learn real-world data engineering workflows used in industry
Course Content Overview
Part 1 - PySpark Fundamentals to Advanced Concepts
This section covers the complete PySpark learning journey, including
• Spark Fundamentals and Distributed Computing
• Spark vs MapReduce
• Spark Architecture and Execution Model
• Jobs, Stages, Tasks, and DAGs
• Docker-Based Spark Setup
• Data Ingestion with Reader APIs
• CSV, JSON, and Parquet Processing
• Schema Inference vs Explicit Schemas
• DataFrame Transformations and Actions
• Spark Joins and Aggregations
• Partitioning and Performance Optimization
• Window Functions and Analytics
• Data Skew Handling
• UDF Internals and Performance Traps
• Data Writing and Storage Strategies
• Managed vs External Tables
• Spark SQL Execution
• Structured Streaming Fundamentals
Part 2 - End-to-End Retail Analytics Project
In the project section, you will build a complete production-style data engineering pipeline using PySpark.
You will learn how to
• Generate and process 1 million+ retail transactions
• Handle data quality issues and invalid records
• Build Bronze, Silver, and Gold data layers
• Implement distributed transformations at scale
• Optimize datasets using Parquet
• Prepare business-ready analytics datasets
• Understand how real data engineering pipelines are structured
• Create portfolio-ready PySpark projects for interviews and jobs
Who this course is for
Beginners who want to start Data Engineering
Python developers moving into Big Data
Aspiring Data Engineers
Analytics Engineers
ETL Developers
Software Engineers interested in Spark
Students preparing for Data Engineering interviews
Anyone who wants practical PySpark experience with real projects