Apache Airflow 3: Advanced DAG Authoring

Apache Airflow 3: Advanced DAG Authoring
Last updated 12/2025
Duration: 4h 55m | .MP4 1920×1080 30fps(r) | AAC, 44100Hz, 2ch | 2.76 GB

Take your Airflow DAGs to the next level with the most advanced features

What you’ll learn
– Design asset-centric DAGs using Airflow 3
– Implement event-driven scheduling to trigger workflows based on external events rather than time-based schedules.
– Build AI workflows with latest Airflow updates using decorators and human in the loop operators

Requirements
– Working knowledge of Apache Airflow 2.x, including basic DAG authoring and execution
– Proficiency in Python programming (intermediate level)
– Experience with basic ETL/data pipeline concepts
– Familiarity with command-line interfaces and basic Linux/Unix commands
– Understanding of basic containerization concepts (Docker)
– Access to a development environment capable of running Apache Airflow 3.x
– Experience with git version control (basic)

Description
Airflow 3: Advanced DAG Authoring

Take your Apache Airflow skills to the next level. This course dives deep into the powerful features of Airflow 3 that separate beginners from production-ready data engineers.

You’ll master the TaskFlow API-from the basics to advanced patterns like dynamic DAG generation, task groups, pools, and resource management. Learn how to build flexible, scalable pipelines using dynamic task mapping with advanced techniques like reduce, expand, and more. Explore modern scheduling strategies including assets, conditional scheduling, and event-driven pipelines with services like AWS SQS. Plus, discover how to integrate AI into your workflows using LLMs, the AI SDK, and human-in-the-loop approval patterns.

What you’ll learn:

Write clean, Pythonic DAGs using the TaskFlow API

Generate DAGs dynamically and reuse tasks like a pro

Master dynamic task mapping for flexible, data-driven workflows

Schedule pipelines using assets, event-driven triggers, and continuous scheduling

Integrate AI and LLMs directly into your Airflow tasks

Implement human-in-the-loop workflows for AI approvals

Every video has its corresponding source code so it’s easy for you to follow along.

Who this course is for:Data engineers and developers with foundational Airflow knowledge who want to write more efficient, maintainable, and production-grade DAGs using Airflow 3’s latest features.

I hope you are ready for the course. Let’s do it!

Marc Lamberti

Who this course is for:
– This course is designed for data engineers who already work with Apache Airflow and want to elevate their DAG authoring skills to an advanced level.
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