Astronomer Webinars

Join us for upcoming online events!

April 3, 2025

Migrating Airflow off ADF? Evaluating Astro vs. Fabric

Join us to learn why top data teams are choosing Astro on Azure instead of Microsoft Fabric for their mission-critical Airflow workloads.

April 23, 2025

Introducing Apache Airflow® 3.0

Be among the first to see Airflow 3.0 in action and get your questions answered directly by the Astronomer team.

Watch Recorded Webinars On-Demand

November 29, 2022

Running Airflow Tasks in Isolated Environments

Airflow DAG authors have multiple options for running tasks in isolated environments. In this webinar, we'll cover everything you need to know.

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November 15, 2022

How to Migrate from Oozie to Airflow: A Guided Walkthrough

In this webinar, we’ll cover everything you need to know about migrating from Oozie to Airflow.

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November 8, 2022

The New DAG Schedule Parameter

We’ll provide a quick refresher of scheduling concepts and discuss how scheduling DAGs is easier and more powerful in newer versions of Airflow.

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November 1, 2022

Batch Inference with Airflow and SageMaker

We’ll walk through using the new SageMaker Async Operators, as well as the new SageMaker OpenLineage integration, for end-to-end ML OPs for batch inference use cases.

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October 25, 2022

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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October 18, 2022

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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October 11, 2022

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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