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Mastering Apache Airflow: Advanced DAGs, Scaling & Hands-on.
Rating: 4.0 out of 5(9 ratings)
92 students

Mastering Apache Airflow: Advanced DAGs, Scaling & Hands-on.

Learn to design, deploy, and scale efficient data pipelines with Apache Airflow through real-world projects.
Created byTech Jedi
Last updated 5/2025
English

What you'll learn

  • How to design, build, and optimize complex DAGs in Apache Airflow.
  • Implement advanced operators, sensors, XComs, SLAs, and custom workflows.
  • Integrate Airflow with cloud platforms, big data technologies, and containerized environments.
  • Monitor, scale, and optimize workflow performance for large datasets.
  • Apply security best practices, role-based access, and data encryption in Airflow pipelines.

Course content

9 sections • 40 lectures • 2h 59m total length

  • What is Apache Airflow?5:06
  • History of Apache Airflow4:01
  • Installation and setup3:03
  • Setting up Apache Airflow Environment Demo4:29

    Set up a portable Apache Airflow environment using a custom docker image and docker compose, persisting configurations and exposing the web UI on port 8080.

Requirements

  • No prior Apache Airflow experience needed – This course covers everything from the basics to advanced topics.
  • Basic Python knowledge is recommended – Familiarity with Python will help in writing DAGs and custom operators.
  • Understanding of data pipelines is helpful – Knowledge of ETL processes or workflow automation concepts can be beneficial.

Description

Master Apache Airflow and become proficient in designing, deploying, and scaling robust data pipelines! This comprehensive course takes you from the fundamentals of Apache Airflow to advanced concepts, ensuring you gain both theoretical knowledge and hands-on experience. You’ll start by understanding what Apache Airflow is, its history, and how to set up a working environment.

You will then dive deep into Directed Acyclic Graphs (DAGs), operators, sensors, and executors, learning how to build and test workflows effectively. Advanced concepts such as XComs, custom operators, trigger rules, SLAs, and data quality checks are explained with practical examples and demo projects. The course also covers logging, monitoring, error handling, performance optimization, and scaling strategies, preparing you to manage large and complex data pipelines efficiently.

Integration with cloud platforms like AWS and GCP, as well as tools like Docker, Kubernetes, and big data technologies, is covered to equip you for real-world scenarios. You’ll explore case studies in finance, e-commerce, healthcare, and social media analytics, showing how Airflow powers mission-critical workflows. Security, authentication, role-based access control, and encryption are also emphasized to ensure safe and compliant data operations.

By the end of this course, you will be able to design, implement, and optimize scalable data pipelines with Apache Airflow, handle advanced use cases, and confidently deploy workflows in production environments. Hands-on demos and practical exercises ensure you can apply these skills immediately in real-world projects.

Who this course is for:

  • Data engineers and developers who want to master workflow orchestration and automation using Apache Airflow.
  • Data scientists and ML engineers looking to deploy scalable ETL pipelines and integrate complex workflows into production environments.
  • Cloud and DevOps professionals seeking hands-on experience with Airflow in AWS, GCP, Docker, and Kubernetes environments.
  • Students and IT professionals interested in learning advanced data pipeline management, DAG optimization, and workflow monitoring.
  • Business analysts and project managers aiming to understand Airflow’s role in data processing and automation for real-world applications.

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