Apache Spark for Data Engineering – Hands-On with PySpark

Go from beginner to building real Spark ETL pipelines using DataFrames and Spark SQL

What you’ll learn
✓ Set up and work with an Apache Spark environment using PySpark to process real-world datasets.
✓ Read data from common formats such as CSV and Parquet
✓ Clean, transform and aggregate data using the Spark DataFrame API & Spark SQL
✓ Build a complete end-to-end Spark ETL pipeline
✓ Understand how Apache Spark works under the hood

Requirements
● Basic programming knowledge : You should be comfortable with basic programming concepts such as variables, functions, and loops (Python or any similar language).
● Basic Python or Scala familiarity (recommended, not mandatory) : Knowing Python or Scala basics will help you follow the examples, but Spark concepts apply to both languages.
● Basic SQL knowledge Understanding simple SQL queries (SELECT, WHERE, GROUP BY) is helpful but not required.
● A computer with internet access A standard laptop or desktop computer is enough. No special hardware is required.

Description
– Why Learn Apache Spark?

Apache Spark is one of the most widely used tools in modern data engineering
It allows you to process large datasets efficiently and build scalable data pipelines used in real-world projects

However, Spark can feel overwhelming at first — especially when courses focus too much on theory or internal details too early

This course is designed to do the opposite

– What This Course Is About

This is a hands-on, practical course focused on how Spark is actually used in real data engineering workflows.

You will learn Spark by writing real PySpark code, working with realistic datasets, and building a complete end-to-end Spark ETL pipeline

The goal is not to turn you into a Spark expert overnight —
the goal is to give you a clear, solid foundation that you can confidently build on.

– What You Will Learn

By the end of this course, you will be able to

• Create and work with a Spark environment

• Read data from common formats such as CSV and Parquet

• Understand schemas and data types

• Transform data using PySpark DataFrames

• Filter data and create derived columns with business logic

• Join multiple datasets together

• Aggregate data using groupBy and aggregation functions

• Use Spark SQL alongside the DataFrame API

• Write processed data back to storage

• Build a complete Spark ETL pipeline from raw data to final output

These are the core skills used in real Spark data engineering projects.

– How This Course Is Structured

• Short, focused lessons

• Strong emphasis on practice and code, not theory

• Progressive difficulty — concepts are introduced only when needed

• A real-world Spark ETL project to tie everything together

Advanced topics such as Spark internals and performance optimization are clearly marked as optional, so beginners can follow the course without feeling overwhelmed

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Who this course is for
■ Developers, data analysts, and engineers who want to learn Apache Spark from scratch and build real-world data pipelines for data engineering roles.