The Terraform configuration used here is a greenfield installation that neither assumes anything about, nor integrates with, existing resources in your Azure account. The Terraform configuration presents an easy way to get started, but you may want to customize this set up before using Feast in production.
This Terraform configuration creates the following resources:
Kubernetes cluster on Azure AKS
Kafka managed by HDInsight
Postgres database for Feast metadata, running as a pod on AKS
Redis cluster, using Azure Cache for Redis
to run Spark
Staging Azure blob storage container to store temporary data
1. Requirements
Create an Azure account and
Install (tested with 0.13.5)
Install (tested with v3.4.2)
2. Configure Terraform
Create a .tfvars file underfeast/infra/terraform/azure. Name the file. In our example, we use my_feast.tfvars. You can see the full list of configuration variables in variables.tf. At a minimum, you need to set name_prefix and resource_group:
my_feast.tfvars
name_prefix = "feast"
resource_group = "Feast" # pre-existing resource group
3. Apply
After completing the configuration, initialize Terraform and apply:
$ cd feast/infra/terraform/azure
$ terraform init
$ terraform apply -var-file=my_feast.tfvars
4. Connect to Feast using Jupyter
After all pods are running, connect to the Jupyter Notebook Server running in the cluster.
To connect to the remote Feast server you just created, forward a port from the remote k8s cluster to your local machine.
kubectl port-forward $(kubectl get pod -o custom-columns=:metadata.name | grep jupyter) 8888:8888
Forwarding from 127.0.0.1:8888 -> 8888
Forwarding from [::1]:8888 -> 8888
You can now connect to the bundled Jupyter Notebook Server at localhost:8888 and follow the example Jupyter notebook.
