Course · Intermediate
Building Data Pipelines with Apache Airflow
You've learned to write Python scripts for data processing, but production data engineering requires coordinating dozens of interdependent tasks that run automatically, recover from failures, and scale as your data grows. Apache Airflow is the industry standard for orchestrating these complex workflows, used by companies from startups to Airbnb and Netflix. This course takes you from Airflow fundamentals through production-grade pipeline development. You'll learn to deploy Airflow in Docker just like production teams do, write clean workflows using the TaskFlow API, implement dynamic parallel processing, and build complete ETL pipelines with database connections, Git-based version control, and CI/CD automation. By the end, you'll create a real-world pipeline that extracts data from live APIs, transforms it with Python, and loads it into MySQL on a schedule—with full monitoring and automated deployment.
- Intermediate friendly
- 8 hrs
- 4 lessons
- Premium
Course overview
Outgrow fragile scripts and cron jobs by orchestrating reliable, production-ready data pipelines with Apache Airflow.
What's inside
4 lessons
- 01 120 minRunning and Managing Apache Airflow with Docker (Part I)
Learn how to run and manage Apache Airflow in Docker to build, test, and visualize data pipelines in a clean, production-like environment.
- 02 120 minRunning and Managing Apache Airflow with Docker (Part II)
Complete your Airflow ETL pipeline with MySQL integration, Git-based DAGs, and CI/CD for production-ready workflow automation.
- 03 120 minIntroduction to Apache Airflow
Learn how Apache Airflow orchestrates complex data workflows with DAGs, tasks, and event-driven automation for scalable data engineering.
- 04 120 minAutomating Amazon Book Data Pipelines with Apache Airflow and MySQL
Learn to build a real Airflow ETL that scrapes Amazon book data, cleans it with Python, loads it into MySQL, and syncs DAGs with Git and CI.
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