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If one out of your hundreds of DAGs fails, how do you know which downstream datasets have become out-of-date? The answer is data lineage. Data lineage is the complex set of relationships between your jobs and datasets. In this webinar, you'll learn how to use OpenLineage to collect lineage metadata from Airflow and assemble a lineage graph - a picture of your pipeline worth way more than a thousand words.
Because Airflow is 100% code, knowing the basics of Python is all it takes to get started writing DAGs. However, writing DAGs that are efficient, secure, and scalable requires some Airflow-specific finesse. In this webinar, you’ll learn the best practices for writing DAGs that will ensure you get the most out of Airflow. We’ll include a reference repo with DAGs you can run yourself with the Astro CLI.
The flexibility and freedom that Airflow offers you is incredible, but to really take advantage of it you need to master some concepts first, one of which has just been released in Airflow 2.2 By the end of the webinar, you will be able to define schedule intervals that you thought were impossible before.
In this informative webinar we will cover everything you need to know about Airflow 2.2. We'll go through all of the new features large and small, as well as show you how to leverage all of the new features and how you can get cleaner and more efficient DAGs as a result
Airflow, by nature, is an orchestration framework, not a data processing framework. At first sight it can be unclear how to test Airflow code. Are you triggering DAGs in the UI to validate your Airflow code? In this webinar we'll demonstrate various examples how to test Airflow code and integrate tests in a CI/CD pipeline, so that you're certain your code works before deploying to production.