| File Name: | Data Engineering Projects with PySpark (2025) |
| Content Source: | https://www.udemy.com/course/data-engineering-projects-with-pyspark-2025/ |
| Genre / Category: | Other Tutorials |
| File Size : | 2.7 GB |
| Publisher: | udemy |
| Updated and Published: | May 27, 2025 |
What you’ll learn
- Set up a complete data engineering stack with Docker, Spark, HDFS, and Airflow
- Build PySpark ETL jobs using DataFrame API and Spark SQL
- Deploy Spark jobs using spark-submit, cron, and Airflow DAGs
- Simulate real team workflows with Git branching, handoff, and rollback
- Organize your project with reusable scripts, env and config files
Want to break into the world of data engineering using PySpark — but don’t want to waste time on abstract theory or outdated tools?
This course is built to teach you exactly what real data engineers do on the job.
We skip the fluff and dive straight into hands-on, project-based learning where you’ll:
- Set up a full modern data engineering stack using Docker
- Write real PySpark ETL jobs using both the DataFrame API and Spark SQL
- Deploy and monitor your code like professionals — using tools like cron, Airflow, and Spark UI
You’ll simulate a real company environment from Day 1. That means:
- Using Git for branching and code tracking
- Creating a team-ready folder structure with scripts/, configs/, env shell, and more
- Learning how to switch between dev and prod configurations
- Even simulating ticket-based deployments, handoffs, and rollback scenarios
What Makes This Course Different?
While most courses focus only on PySpark syntax, this course shows you:
- Where Spark fits in real-world pipelines
- How to structure your codebase to be reusable and production-friendly
- How to actually deploy jobs using tools like spark-submit, cron jobs, and Airflow DAGs
- How to debug and tune Spark jobs using logs, Spark UI, caching, and skew handling
This isn’t just a “learn PySpark” course — this is a “build production data pipelines like a real engineer” course.
What Will You Learn?
- How to build and schedule Spark jobs like a data engineer
- How to write clean, modular PySpark code using industry-standard practices
- How to deploy your jobs using cron and Apache Airflow
- How to monitor, debug, and optimize jobs using Spark UI
- How to use Docker to set up Spark, HDFS, Airflow, and Jupyter — all in one go
You’ll complete two real-world projects by the end of the course — both designed to reflect how data teams operate in actual companies.
Who Is This Course For?
- Aspiring data engineers looking for real project experience
- Python developers or analysts transitioning into data engineering roles
- Students and freshers looking to build portfolio-ready projects
- Professionals preparing for interviews or on-the-job Spark work
- Anyone who wants to learn PySpark the practical way
Requirements
- Basic Python knowledge
- Familiarity with SQL is helpful (but not required)
- No prior Spark, Airflow, or Docker experience needed — everything is explained step by step
- A system with at least 8 GB RAM (for Docker-based setup)
By the end, you’ll be confident writing PySpark ETL jobs and deploying them the same way real companies do it in production.
This course is not just about learning Spark — it’s about learning how to think like a data engineer.
DOWNLOAD LINK: Data Engineering Projects with PySpark (2025)
Data_Engineering_Projects_with_PySpark___40_2025__41_.part1.rar – 1.5 GB
Data_Engineering_Projects_with_PySpark___40_2025__41_.part2.rar – 1.2 GB
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