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- Data
Build Data Pipelines with Apache Airflow 3
Apache Airflow 3 is an open-source platform for authoring, scheduling, and monitoring data pipelines as Python code. This path takes you from your first Dag to production-ready workflows. You'll build pipelines with the Task SDK, schedule them by time or by data availability using assets and event-driven triggers, handle failures and dynamic workloads, connect to databases and APIs, deploy and troubleshoot Airflow in production, and orchestrate AI workloads with the Common AI Provider.
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\ **This learning path is actively in production. More content will be added to this path as it gets published and becomes available in the library. To view the full list of content to be included in this Path, click the "About this path" tab below.**
Content in this path
Hands-on Practice with Apache Airflow 3
Gain practical, hands-on experience with the follow Apache Airflow 3 labs.
Try this learning path for free
What You'll Learn
- How to get up and running with Apache Airflow
- How to design and run workflows in Apache Airflow 3
- How to use Apache Airflow 3's Common AI Provider to build AI workflows
- Up and Running with Apache Airflow 3
- Design and Test Pipelines with Apache Airflow 3
- Schedule and Trigger Pipelines with Apache Airflow 3
- Add Dynamic Behavior and Resilience to Pipelines with Apache Airflow 3
- Integrate Apache Airflow 3 with External Systems
- Deploy, Monitor, and Troubleshoot Apache Airflow 3
- Build AI and Agentic Pipelines with Apache Airflow 3
- Write and Run a Dag with the Task SDK in Apache Airflow 3
- Pass Data Between Tasks with XComs in Apache Airflow 3
- Trigger Pipelines with Assets in Apache Airflow 3
- Integrate a Database with Apache Airflow 3
- Learners interested in this path should be comfortable writing Python functions and working from the command line, and familiar with basic SQL. No prior Apache Airflow experience is required, but core knowledge of data orchestration and workflow management is helpful.
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