![]() When your environment is up and running, the Google Cloud UI is clean and hassle-free: it just links to the DAG folder and to your Airflow webserver, which is where you’ll be spending most of your time. You could also do deployment programmatically by using Google Cloud’s gcloud. Of course, drag-and-drop is not the only option. Within seconds the DAG appears in the Airflow UI. If no StorageClass is designated as the default StorageClass, then the deployment fails. ![]() The Airflow deployment process attempts to provision new persistent volumes using the default StorageClass. This means you could literally drag-and-drop the contents of your DAG folder to deploy new DAGs. Solution Deployment and Validation Details AI Inferencing at the Edge - NetApp with Lenovo ThinkSystem - Solution Design. Your DAGs folder sits in a dedicated bucket in Google Cloud Storage. You can also easily list your required Python libraries from the Python Package Index (PyPI), set environment variables, and so on.ĭeployment is simple. If you have a Google Cloud account, it’s really just a few clicks away (plus ~20 minutes of waiting for your environment to boot). This is by no means an exhaustive evaluation - it’s simply my first impression of Cloud Composer. So, below is a very brief write-up of the experience testing out Cloud Composer. The nice thing about hosted solutions is that you as a Data Engineer or Data Scientist don’t have to spend that much time on DevOps - something you might not be very good at (at least I’m not!). As I had been looking at hosted solutions for Airflow, I decided to take Cloud Composer for a spin this week.
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