The Complete Data Engineering Bootcamp with PySpark (2025) | Udemy [Update 06/2025]
English | Size: 2.6 GB
Genre: eLearning[/center]

Learn how real data engineers build and deploy PySpark pipelines with Airflow, Git, and production-grade workflows

What you'll learn
Set up a complete data stack: Docker, Spark, Airflow, HDFS, Jupyter
Build and deploy PySpark ETL jobs using DataFrame API and Spark SQL.
Build & deploy PySpark pipelines with Airflow and cron
Organize your project professionally with scripts, config files, environment shells, and Git.
Simulate authentic data engineering workflows: Git branching, code reviews, ticket-based deployments.

Want to become a data engineer using PySpark - without wasting time on abstract theory or outdated tools?
This course shows you exactly what professional data engineers do, using the tools, structures, and workflows used in real production environments.

What You'll Learn Through Real Projects:

Set up a complete data engineering stack with Docker, Spark, Airflow, HDFS, and Jupyter.

Write and deploy production-ready PySpark ETL jobs using DataFrame API and Spark SQL.

Automate and schedule pipelines using cron, Airflow DAGs, and monitor them with Spark UI.

From Day 1, You'll Work Like a Real Data Engineer:

Master Git branching, merging, and real-world version control workflows.

Structure your projects professionally: scripts/, configs/, env shell, and reusable modules.

Seamlessly switch between development and production environments.

Simulate ticket-based deployments and team collaboration - just like real companies.

What Makes This Course Different?

Most PySpark courses teach only syntax. This course prepares you for real-world data pipelines:

Understand exactly where Spark fits in production data workflows.

Build modular, production-ready codebases.

Deploy jobs using spark-submit, cron, and Airflow.

Monitor, debug, and optimize pipelines using Spark UI, logs, caching, and tuning techniques.

This course is a practical guide to building and deploying real data pipelines - like a professional data engineer.

You Will Specifically Learn:

Set up a Docker-based data engineering environment with Spark, Airflow, HDFS, and Jupyter.

Build reliable PySpark ETL jobs using DataFrames and Spark SQL.

Automate pipelines with spark-submit, Airflow DAGs, and cron scheduling.

Organize your code with real-world project structures and Git workflows.

Complete two full real-world data engineering projects - exactly how data engineering teams work.

By the end of this course, you'll have practical, production-grade skills that real data engineers use daily.

Who this course is for:
Aspiring data engineers who want hands-on, realistic project experience.
Python developers or analysts transitioning into data engineering roles.
Students and self-learners seeking portfolio-worthy PySpark projects.
Professionals preparing for real-world Spark-based roles and interviews.

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