Course

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.

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Part of the Data Engineer (Python) path.

4.8 (359 reviews)

9 learners enrolled in this course.

Intermediate friendly
8 hours
Self paced
4 lessons

“The learning paths on Dataquest are incredible. They give you a direction through the learning process – you don’t have to guess what to learn next.”

Otávio Silveira

Data Analyst @ Hortifruti

Building Data Pipelines with Apache Airflow

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Course overview

Manual scripts and cron jobs break down as data pipelines grow complex. Apache Airflow brings order to chaos through workflow orchestration—ensuring tasks run in the right order, at the right time, with proper failure handling and monitoring. This course teaches you to build production-grade data pipelines the way professional teams do. You’ll start by understanding orchestration concepts and Airflow’s architecture, then deploy a complete Airflow environment in Docker. Using the TaskFlow API, you’ll build increasingly sophisticated workflows: from simple ETL processes to pipelines with dynamic parallel processing and database connections. You’ll integrate Git-based version control and GitHub Actions CI/CD for automated deployment. Finally, you’ll build a real-world pipeline that scrapes Amazon book data, cleans it with Python, and loads it into MySQL on a schedule—complete with monitoring and alerting. By the end, you’ll have the skills to orchestrate complex data workflows reliably at scale.

Key skills

Understanding workflow orchestration and how Airflow structures pipelines through DAGs and tasks
Deploying Airflow in Docker for development and production-like environments
Writing clean, maintainable DAGs using the TaskFlow API
Implementing parallel processing with Dynamic Task Mapping for scalable workflows
Managing database connections and credentials securely in Airflow
Setting up version control and automated deployment for DAGs using Git and GitHub Actions
Building complete ETL pipelines with monitoring, retries, and alerting
Extracting data from real-world APIs and loading it into databases on automated schedules

Course outline

Building Data Pipelines with Apache Airflow [4 lessons]

Introduction to Apache Airflow 2h

Lesson Objectives

Understand workflow orchestration and its role in data pipelines
Explain Apache Airflow's core components: DAGs, tasks, and scheduler
Navigate and interpret the Airflow Web UI effectively
Differentiate between Airflow orchestration and traditional cron scheduling
Identify common Airflow use cases: ETL, ML, and analytics

Running and Managing Apache Airflow with Docker (Part I) 2h

Lesson Objectives

Deploy Apache Airflow using Docker Compose efficiently
Build ETL pipelines with TaskFlow API decorators
Implement dynamic task mapping for parallel processing
Debug workflows using Airflow Web UI and logs
Configure DAG scheduling, retries, and dependency management

Running and Managing Apache Airflow with Docker (Part II) 2h

Lesson Objectives

Complete ETL lifecycle by adding Load phase to pipeline
Configure and manage MySQL database connections in Airflow
Switch from CeleryExecutor to LocalExecutor for simplified architecture
Implement Git-based DAG management using git-sync for automation
Set up CI/CD validation pipeline with GitHub Actions

Automating Amazon Book Data Pipelines with Apache Airflow and MySQL 2h

Lesson Objectives

Build real-world ETL pipelines using Apache Airflow TaskFlow API
Extract and transform live data from web sources systematically
Implement Git-based DAG management with automated git-sync integration
Configure CI/CD validation workflows using GitHub Actions pipelines
Design production-ready data pipelines with monitoring and alerting

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