DAG Writing Best Practices
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Whether you’re writing traditional ELT/ETL pipelines or complex ML workflows, you’ll learn how to make Airflow work best for your use case.
Astronomer Webinars
Join us for upcoming online events!
Introduction to Observability for Data Pipelines
In this webinar, we’ll give an overview of data observability for Airflow, including topics like tracking data lineage, detecting anomalies, monitoring data quality, and insights into the performance of your data operations.
The State of Airflow 2025
Our panel of experts will discuss the state of Airflow in 2025, including trends and results from the recent annual Airflow survey.
Watch Recorded Webinars On-Demand
4 Things To Consider When Deciding How to Run Airflow
Teams can run Airflow themselves, go with a managed infrastructure option like MWAA and GCC, or choose Astro. Whether you’ve been running Airflow in production for years or if you’re just thinking about the right Airflow strategy is for your team, we’ll go through all the options available.
Astro Product Roadmap
Join the Astronomer team to learn more about upcoming Astro features and our product roadmap.
How to Orchestrate Machine Learning Workflows with Airflow
In this webinar on machine learning orchestration with Apache Airflow®, we’ll provide an overview of machine learning orchestration, its importance in managing ML workflows, and how it fits in with MLOps.
How to Orchestrate Databricks Jobs Using Airflow
In this “Live with Astronomer” session, we’ll dive into Cosmos, an open-source framework developed by Astronomer for dynamically generating Airflow DAGs from other tools.
How to Automate and Scale Data Ingestion with Fivetran and Airflow
In this webinar, we’ll dive into the newly updated Fivetran provider and discuss the benefits of using Airflow and Fivetran together for your ELT pipelines. We’ll cover how to implement the available Fivetran operators to orchestrate sync jobs, how to leverage asynchronous functionality for cost savings, and how to use lineage generated from Fivetran tasks to get insight into your data pipelines.
How to easily test your Airflow DAGs with the new dag.test() function
In this “Live with Astronomer” session, we’ll dive into the new `dag.test()` function, which allows you to debug DAGs directly in your IDE in a single, serialized Python process. We’ll show how this function lets you quickly iterate and debug errors during your DAG development, and even test argument-specific DAG runs.
How to Improve Data Quality with Airflow's Great Expectations Operator
In this webinar, Airflow Engineering Advocate Benji Lampel will demonstrate the new features of the Great Expectations Operator, which make Great Expectations more Airflow-centric and simpler to use.
How to Save Money using Airflow’s asynchronous Azure operators
We’ll dive into the newly developed asynchronous Azure operators that offer cost savings and greater scalability.
Simplified DAG Authoring with New Airflow Features
In this webinar, we’ll cover best practices for implementing important open-source features and frameworks that will simplify the DAG-authoring process.
