The Complete Data Engineering Bootcamp with PySpark(2026)

Name: The Complete Data Engineering Bootcamp with PySpark(2026)
Rating: 4.5 (87 reviews)

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

Created byChandra Venkat

Last updated 6/2026

English

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.

Course content

8 sections • 37 lectures • 5h 44m total length

Why Spark Solves Real ETL Problems2:02
Learn why PySpark is preferred for large-scale data pipelines over traditional tools.
Spark’s Role in Data Pipelines2:20
See where Spark fits in modern data workflows, from raw to processed data
Spark Jobs, Stages, DAGs — Quick Intro4:12
Get a fast overview of how Spark executes jobs internally

What You’ll Set Up – Spark + Airflow + HDFS in Docker1:42
Understand the tools you’ll set up and how they work together.
Install WSL2 + Docker5:18
Quickly set up Docker on your machine, even on Windows using WSL2.
Create Docker Compose Setup2:54
Build your complete data engineering environment with a single Compose file
One Command: Launch Spark, Airflow, HDFS, Jupyter12:55
Run your entire stack with one command like a real dev.

Important (Read Before Running Docker)
If you see this error while running docker compose up:
“permission denied while connecting to Docker daemon”
or
“docker.sock: connect: permission denied”

Run this once inside Ubuntu:
sudo usermod -aG docker $USER

Then close Ubuntu completely and open it again.
After this, docker compose up will work normally.
Docker Compose Setup - Troubleshooting Guide10:32
Explore Spark UI, Airflow UI, HDFS UI5:59
Learn how to navigate the UIs you’ll use to monitor jobs and data
Explore HDFS10:27
Upload datasets into HDFS for real-world big data pipelines.
Use PySpark Shell4:32
Start your first interaction with PySpark and explore basic commands

Load & Explore Files in Jupyter and PySpark Shell14:41
Load and inspect datasets interactively using Jupyter + PySpark.
Build ETL Logic Using DataFrame API (Joins, Transforms, Write)17:43
Create ETL pipelines using PySpark’s powerful Spark DataFrame API.
Perform Same ETL in Spark SQL11:26
Create ETL pipelines using PySpark’s Spark SQL
Write Clean PySpark Scripts9:12
Organize your ETL logic into reusable, clean scripts for production.
Submit Jobs Using spark-submit3:42
Run PySpark jobs in cluster mode using spark-submit.
(Optional)How to Navigate Spark-apps10:57

Understand Sales ETL Requirement10:47
Understand business requirements behind real-world sales data pipelines
Build the Sales ETL Pipeline Using Pyspark Shell20:43
Develop a full ETL pipeline for sales data using PySpark.
Create a Reusable PySpark Script11:02
Package your ETL logic into a modular, team-friendly script.
Dynamic Shell Script for Deployment11:20
Create a dynamic shell script to deploy ETL jobs easily.
Schedule Daily Run with Cron (Simulate Lightweight Prod)9:55
Set up a daily job scheduler with cron, simulating production workflows.
Wrap-Up and Key Learnings13:19
Summarize what you built and how it fits into real production setups.
Notes/Commands used in Sales ETL Pipeline3:14

Understand Customer ETL Requirement4:01
Understand the architecture behind customer data pipelines.
Build Pipeline using Jupyter notebook22:29
Build a production-ready customer ETL job with PySpark.
Convert Workflow into Pipeline Script11:21
Create Shell Wrapper script13:06
Set up wrapper scripts to automate job deployments.
Automate Pipeline with Airflow22:15
Design, schedule, and run your pipeline using Airflow DAGs.
Wrap-Up and Lessons Learned4:54
Review the pipeline and understand real-world deployment lessons.

Why Dev vs Prod Matters – Company Best Practices3:11
Learn the importance of separating development, QA, and production environments
Create Env shell to make Pipeline Dynamic3:17
Create dynamic shell scripts to switch environments easily.
Switch Between Dev and Prod25:36
Practice switching modes without changing your codebase.
Your Production-Ready Pipeline Setup4:24
Build a flexible setup that real companies use for pipeline management.

Requirements

Basic Python knowledge
Familiarity with SQL is helpful but not mandatory.
No prior experience with Spark, Docker, or Airflow is required; everything is taught step-by-step
A computer with at least 8 GB RAM (12 GB recommended) and 40 GB free disk space (50 GB recommended)
A good internet connection

Description

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.

The Complete Data Engineering Bootcamp with PySpark(2026)

What you'll learn

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Course content

Start Your Data Engineering Journey3 lectures • 9min

Set Up Your Data Engineering Stack8 lectures • 54min

Build and Test PySpark ETL Jobs6 lectures • 1hr 8min

Real Project: Sales ETL Pipeline (Cron)7 lectures • 1hr 20min

Real Project: Customer ETL Pipeline (Airflow & HDFS)6 lectures • 1hr 18min

Production Ready: Dev vs Prod Pipelines4 lectures • 36min

Workflows — Git, Handoffs, Deployments2 lectures • 19min

Thank you!!1 lecture • 1min

Requirements

Description

Who this course is for: