Monitor Your DAGs with Airflow Notifications
Anytime you’re running business critical pipelines, you need to know when something goes wrong. Airflow has a built in notification system that can be used to throw alerts when your DAGs fail, succeed, or anything in between. In this webinar, we’ll do a deep dive into how you can customize your notifications in Airflow to meet your needs.
Intro to Airflow for ETL with Snowflake
ETL is one of the most common data engineering use cases, and it's one where Airflow really shines. In this webinar, we'll cover everything you need to get started as a new Airflow user, and dive into how to implement ETL pipelines as Airflow DAGs.
Getting Started With the Official Airflow Helm Chart
The official helm chart of Apache Airflow is out! 🥳 The day of wondering what Helm Chart to use in production is over. Now, you only have one chart maintained and tested by Airflow PMC members as well as the community. It's time to get your hands on it and take it for a spin! At the end of the webinar, you will have a fully functional Airflow instance deployed with the Official Helm Chart and running within a Kubernetes cluster locally.
Using Dynamic DAGs with Airflow
The simplest way of creating an Airflow DAG is to write it as a static Python file. However, sometimes manually writing DAGs isn't practical. Maybe you have hundreds or thousands of DAGs that do similar things, with just a parameter changing between them. Or maybe you need a set of DAGs to load tables, but don't want to manually update DAGs every time those tables change. In these cases, and others, it can make more sense to dynamically generate DAGs. Because everything in Airflow is code, you can dynamically generate DAGs using Python alone. In this webinar, we'll talk about when you might want to dynamically generate your DAGs, show a couple of methods for doing so, and discuss problems that can arise when implementing dynamic generation at scale.
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