A DataCamp course on workflow orchestration with Apache Airflow — building DAGs, scheduling and automating data pipelines, and monitoring runs. Hands-on in Python, it is a practical introduction for data engineers and analysts who need to automate and schedule reliable pipelines.
Good for: Learning to orchestrate data pipelines with Airflow.
Less suitable if: You do not build data pipelines or lack Python.
Requirements: Python; some data-pipeline context helps.
Realistic time: Around 4 hours.
About this course
Introduction to Apache Airflow in Python teaches workflow orchestration: building DAGs (Directed Acyclic Graphs), scheduling and automating data pipelines, monitoring and debugging runs, and — in the final chapter — combining triggers, branching logic, and human-approval gates into a complete production-style pipeline. It's kept current, explicitly updated to Apache Airflow 3.1.6.
What you'll learn
Build and run DAGs (Directed Acyclic Graphs) in Airflow
Schedule and automate data pipelines
Use sensors, executors, and XCom for task communication
Monitor, debug, and troubleshoot Airflow workflows
Apply Jinja templating and Airflow variables
Build a complete production pipeline with branching and human approval
This course includes
4h
On-demand video
Yes
Certificate
Yes
Mobile access
English
Language
What it costs
DataCamp runs on a subscription — roughly $14/month billed annually (more month-to-month), with the first chapter of each course free to try. A certificate of completion is included with the subscription.
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