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:
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Data Engineers looking to upgrade their skills to Apache Airflow 3.0
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Backend Developers and DevOps Engineers managing data orchestrations
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Data Architects designing enterprise-grade, resilient pipeline infrastructure
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Analytics Engineers wanting to automate scaling data workloads
What you will learn:
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Build dynamic workflows with Airflow 3 dynamic task mapping
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Eliminate worker bottlenecks using async deferrable operators and triggers
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Design self-healing pipelines with exponential backoffs and callbacks
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Architect high-concurrency event-driven DAGs in Apache Airflow 3.0
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Optimize cloud infrastructure costs by freeing up idle worker slots
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Implement circuit breakers, dead letter queues, and SLA monitoring
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Handle unpredictably sized data payloads smoothly at runtime
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Aggregate mapped task outputs efficiently across complex workflows
Requirements:
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Fundamental knowledge of Python programming
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Basic understanding of data engineering concepts and SQL
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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.
