This course contains the use of artificial intelligence.

Stop building brittle, static data pipelines that break under changing workloads and waste expensive cluster resources.

In this hands-on, practical course, you will master the next-generation features of Apache Airflow 3.0 to construct fully dynamic, event-driven, and resilient data pipelines built for enterprise scale. You will learn how to scale tasks dynamically at runtime, leverage deferrable operators to reduce infrastructure costs, and build automated self-healing mechanisms into every DAG you deploy.

Who this course is for:

• Data Engineers looking to upgrade their skills to Apache Airflow 3.0

• Backend Developers and DevOps Engineers managing data orchestrations

• Data Architects designing enterprise-grade, resilient pipeline infrastructure

• Analytics Engineers wanting to automate scaling data workloads

What you will learn:

• Build dynamic workflows with Airflow 3 dynamic task mapping

• Eliminate worker bottlenecks using async deferrable operators and triggers

• Design self-healing pipelines with exponential backoffs and callbacks

• Architect high-concurrency event-driven DAGs in Apache Airflow 3.0

• Optimize cloud infrastructure costs by freeing up idle worker slots

• Implement circuit breakers, dead letter queues, and SLA monitoring

• Handle unpredictably sized data payloads smoothly at runtime

• Aggregate mapped task outputs efficiently across complex workflows

Requirements:

• Fundamental knowledge of Python programming

• Basic understanding of data engineering concepts and SQL

• Familiarity with basic Apache Airflow concepts (DAGs, tasks, operators)

Final Project Description: For your capstone project, you will build an end-to-end, market-ready e-commerce data pipeline in Apache Airflow 3. Your pipeline will dynamically discover multi-region data stores, wait asynchronously for third-party file uploads using deferrable triggers, process store data in parallel, and automatically handle simulated network failures using advanced retry strategies and failure callbacks.