Introducing Apache Airflow 2.9
- Kenten Danas Manager, Developer Relations
The Airflow 2.9 release brings significant enhancements to user-favorite features like data-aware scheduling, dynamic task mapping, and object storage.
The Airflow 2.9 release brings significant enhancements to user-favorite features like data-aware scheduling, dynamic task mapping, and object storage.
An introduction to testing strategies, best practices, and implementation techniques.
Our beta cohort of 10 is now joined by 23 hand-selected individuals who, we believe, truly embody what it means to champion the Apache Airflow Project.
Learn how to use Airflow’s operators, custom extractors, and inlet/outlet arguments to send lineage to your data observability tool.
Learn how Astro can help you understand, communicate, and solve pipeline problems.
Data lineage is having a moment. Julien Le Dem, Co-Founder of OpenLineage and Chief Architect at Astronomer, looks at the drivers behind a notable increase in lineage adoption.
The Airflow-driven data quality checks that we use at Astronomer ensure bad data is found and fixed quickly.
Learn what the new upgraded Astro Python SDK 1.1 offers to Airflow users.
Learn how Astronomer’s ambitious vision for data orchestration has led to a fully coordinated ecosystem that runs consistently and reliably, with minimal overhead and with full control, visibility, and governance of data operations.
Airflow 2.4 lets you break down big, monolithic pipelines — in which long-running tasks can delay time-sensitive ones — into “micropipelines” that let you tune your data ecosystem and make critical data products available on time.
How a technical ecosystem built by Astronomer’s data science team is empowering internal groups to own and share data.
The new data-driven scheduling functionality in Airflow 2.4 eliminates a lot of complexity and toil. See how Astronomer is applying this useful feature.
Try Astro free for 14 days and power your next big data project.