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Track

Airflow Fundamentals

Updated 07/2026
Build production data pipelines with Apache Airflow. Go from basic Dags to scheduled, tested workflows you can trust to run on their own.
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Track Description

Airflow Fundamentals

Get hands-on with Apache Airflow, the industry standard for orchestrating data and AI workflows, and learn to take your pipelines from a first draft to something you can run in production.You'll start with the fundamentals of Airflow, building your first Dags and learning how scheduling, error handling, and reporting remove the manual work from delivering data. From there, you'll implement more complex data engineering pipelines and automate them in a repeatable way.Next, you'll move to modern Airflow, authoring Dags with the TaskFlow API, scheduling them based on data using Assets and Asset Partitions, and making them reliable with retries, callbacks, and tests. You'll also build an end-to-end SQL ETL pipeline and add data quality checks directly in Airflow, so the data your pipeline produces stays trustworthy.Finally, a hands-on tutorial shows you how to orchestrate an AI workflow, turning a simple LLM prototype into a reliable, scheduled pipeline. You'll come away able to build and run pipelines that hold up in production.

Prerequisites

There are no prerequisites for this track

  • Course

    1

    Introduction to Apache Airflow in Python

    Learn how to implement and schedule data engineering workflows.

  • Course

    2

    Building Data Pipelines with Airflow

    Author Dags with the TaskFlow API, asset-based scheduling, and deferrable sensors, and run an end-to-end SQL ETL pipeline with quality checks.

  • Resource

    bonus

    Orchestrating AI Pipelines With Airflow

    Learn how to turn a simple LLM prototype into a reliable, scheduled AI workflow with Apache Airflow.

Airflow Fundamentals
2 Courses
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