Coursera
Subscription
DataCampBuilding Data Pipelines with Airflow
4.7(12)ADVANCEDEnglish
Korshub's take
Mastering Modern Airflow
This advanced course is designed for data engineers looking to move beyond basic DAG creation and into production-grade orchestration. By focusing on Airflow 3.2, the curriculum ensures you are learning the most current industry standards rather than outdated methods.
Key Learning Outcomes
- Modern Workflow Authoring: Master the TaskFlow API for cleaner, more efficient DAG development.
- Data-Driven Scheduling: Utilize Assets and Asset Partitions to schedule tasks based on data availability.
- Production Reliability: Implement robust error handling through retries, callbacks, and rigorous testing.
- End-to-End ETL: Build a complete SQL ETL pipeline using DuckDB, featuring integrated data quality checks without relying on external libraries.
If you want to transform a simple prototype into a reliable, production-ready pipeline, this is the deep dive you need.
About this course
Want to take your Airflow skills further? This course uses Airflow 3.2, so you'll learn the latest way of doing things. You'll author Dags with the TaskFlow API, schedule them based on data using Assets and the new Asset Partitions, and make them reliable with retries, callbacks, and tests. In the final chapter, you'll build an end-to-end SQL ETL pipeline on DuckDB and add data quality checks directly in Airflow, without any third-party libraries, so the data your pipeline produces stays trustworthy. By the end, you'll know how to take a pipeline from a prototype to something you can actually run in production.
Access
SubscriptionAdvertisement
