Astronomer Webinars

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Airflow best practices: debugging and testing

Hosted By

  • Kenten Danas
  • Marc Lamberti

In this webinar, you'll learn how to easily and effectively debug your DAGs when an error inevitably occurs, and implement testing so issues don’t make it to production.

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Watch Recorded Webinars On-Demand

Data-Aware Scheduling with the Astro Python SDK

Live with Astronomer dives into implementing data-aware scheduling with the Astro Python SDK. The new Airflow Datasets feature allows you to schedule DAGs based on updates to your data and easily view cross-DAG relationships. This feature is part of the Astro Python SDK, so it requires almost no effort from the DAG author to implement. We'll show you everything you need to do (and don't need to do) to take advantage of Datasets.

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Dynamic Task Mapping on Multiple Parameters

On October 25, Live with Astronomer will dive into updates to the dynamic task mapping feature released in Airflow 2.4. We’ll show a couple of new methods for mapping over multiple parameters, and discuss how to choose the best mapping method for your use case.

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Dynamic Tasks in Airflow

With the releases of Airflow 2.3 and 2.4, users can write DAGs that dynamically generate parallel tasks at runtime. In this webinar, we’ll cover everything you need to know to implement dynamic tasks in your DAGs.

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Data Driven Scheduling

In this session, Live with Astronomer explores the new datasets feature introduced in Airflow 2.4. We’ll show how DAGs that access the same data now have explicit, visible relationships, and how DAGs can be scheduled based on updates to these datasets.

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