Simply speaking, Airflow will periodically check the git repository and if it detects changes, it will pull them, automatically updating your DAGs without any additional work. Git SyncĪirflow's git syncing is a very handy tool to enable GitOps over your DAGs. Most things will depend on your particular use case, but here we will take a look at some considerations. Of course, practically, there is a lot of configuration needed.
#Kubernetes airflow docker install
Theoretically speaking, all you need to do is run the following command from your command line helm install airflow -namespace airflow apache-airflow/airflow HelmĪirflow contains an official Helm chart that can be used for deployments in Kubernetes. Let's look at some of its options and how it can be used along with MLflow on Kubernetes. In combination with EKS, Airflow on Kubernetes can be a reliable, highly scalable tool to handle all your data. Since its initial release in 2015, it gained enormous popularity and today it's a go-to tool for many data engineers. Airflow is an open-source tool that allows you to programmatically define and monitor your workflows. AirflowĪt Pilotcore, we often use Airflow pipelines in our machine learning projects along with MLflow for model management. After your cluster is up and running it's time to deploy the first resources to it, in our case Airflow and MLflow.
In the previous article, we described the deployment of your own Kubernetes cluster in AWS using the Elastic Kubernetes Service (EKS). Want to get up and running fast in the cloud? Contact us today.
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