Google Cloud published a blog post about how to run Apache Airflow on Google Cloud. Apache Airflow is a popular choice for running a complex set of tasks, such as Extract, Transform, and Load (ETL) or data analytics pipelines. Apache Airflow uses a Directed Acyclic Graph (DAG) to order and relate multiple tasks for your workflows, including setting a schedule to run the desired task at a set time, providing a powerful way to perform scheduling and dependency graphing.
The article explores three different ways to run Apache Airflow on Google Cloud, discussing the pros and cons of each approach.
* **Compute Engine:** This is the most straightforward way to run Airflow on Google Cloud. It involves installing Airflow on a Compute Engine VM instance. This approach is relatively easy to set up and inexpensive, but it requires you to manage the VM yourself.
* **GKE Autopilot:** This is a more managed way to run Airflow on Google Cloud. It involves deploying Airflow to a GKE Autopilot cluster. This approach offers more scalability and reliability than running Airflow on Compute Engine, but it also requires more Kubernetes knowledge.
* **Cloud Composer:** This is the easiest way to run Airflow on Google Cloud. Cloud Composer is a fully managed service that takes care of managing the underlying Airflow infrastructure for you. This approach offers the easiest way to get started with Airflow, but it is also the most expensive.
The article also provides step-by-step instructions on how to deploy Airflow using each of these methods.
I found this article to be very helpful. It provided a great overview of the different options for running Airflow on Google Cloud. I also appreciated the detailed instructions on how to deploy Airflow using each of these methods.
I would recommend this article to anyone looking to run Apache Airflow on Google Cloud.
