Terraform can be used to provision, manage, and scale Databricks environments; it allows infrastructure to be defined as code (IaC), enabling version control, reproducibility, and automation. Using Terraform to deploy Databricks workspace streamlines resource provisioning, access management, and workspace configuration to ensure consistency across environments and simplify scaling or modifications. This article describes how to Automate Databricks Infrastructure with Terraform.
Terraform Environment Setup
Databricks is a cloud-based tool built on Apache Spark used in machine learning, big data analytics, and data engineering to enable AI model deployment, fast data processing, and collaboration. Its scalable Delta Lake infrastructure supports its efficient, secure data management.
To manage Databricks infrastructure with Terraform, you have to install and configure some of the systems; the first step is installing Terraform and the Databricks provider. Download Terraform from the official website and ensure it is available in the system’s path, and then initialize a Terraform working directory by creating a new directory and running :
terraform init
Authentication in Databricks is configured using the credentials from cloud providers. A service principal is used to set up authentication for Azure:
provider "databricks" {
host = "https://<your-databricks-instance>"
azure_client_id = var.azure_client_id
azure_client_secret = var.azure_client_secret
azure_tenant_id = var.azure_tenant_id
}
An Access key or service accounts will be required for AWS and GCP
Databricks Infrastructure with Terraform Setup
Terraform allows configuration using HCL (Hashicorp Configuration Language). and configurations are made in the primary configuration file, main.tf, declares a Databricks workspace:
resource "databricks_workspace" "example" {
name = "example-workspace"
resource_group = "example-rg"
location = "East US"
}
Configuring clusters within a workspace requires some important setup, like versioning, autoscaling, and specific nodes:
resource "databricks_cluster" "example" {
cluster_name = "example-cluster"
spark_version = "12.0.x-scala2.12"
node_type_id = "Standard_D3_v2"
autotermination_minutes = 20
autoscale {
min_workers = 2
max_workers = 8
}
}
Access Control and Permissions
Terraform will be able to control user roles and access control lists using the Databricks permission control user and group access
resource "databricks_group" "data_scientists" {
display_name = "Data Scientists"
}
resource "databricks_user" "analyst" {
user_name = "[email protected]"
groups = [databricks_group.data_scientists.id]
}
Integrating Terraform with a cloud IAM system like Microsoft Entra Id ensures authentication and access policies are in line with enterprise governance best practices.
Deploying Databricks Jobs with Terraform
Databricks jobs are used to automate batch and streaming workloads; the databricks_job resources define the job execution parameters:
resource "databricks_job" "example_job" {
name = "Example Job"
new_cluster {
spark_version = "12.0.x-scala2.12"
node_type_id = "Standard_D3_v2"
num_workers = 4
}
notebook_task {
notebook_path = "/Shared/example_notebook"
}
}
Scheduling jobs and dependencies are managed within Terraform, allowing seamless automation of recurring tasks.
Versioning with Terraform
Terraform uses a state file to track all deployed resources to ensure collaborations and prevent conflicts, so remote state storage must be configured.
terraform {
backend "azurerm" {
resource_group_name = "terraform-state-rg"
storage_account_name = "tfstateaccount"
container_name = "tfstate"
key = "databricks.tfstate"
}
}
This ensures changes made to infrastructure are managed safely using terraform plan and terraform apply, preventing unintended changes.
Automating Deployment with CI/CD
To ensure infrastructure with consistency, Terraform needs to integrate with CI/CD; a Github action pipeline can be used for automated deployments:
name: Terraform Deployment
on: [push]
jobs:
deploy:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- name: Setup Terraform
uses: hashicorp/setup-terraform@v1
- name: Terraform Init
run: terraform init
- name: Terraform Apply
run: terraform apply -auto-approve
This pipeline can also be configured in GitLab CI/CD or Azure DevOps for streamlined deployments.
Monitoring and Scaling Databricks infrastructure
Databricks environments require monitoring for performance and cost efficiency; Terraform can be used to configure monitoring solutions:
resource "databricks_instance_profile" "monitoring" {
instance_profile_arn = "arn:aws:iam::123456789012:instance-profile/DatabricksMonitor"
}
Autoscaling is enabled on the clusters to optimize resource utilization without over-provisioning:
autoscale {
min_workers = 4
max_workers = 16
}
Errors and debugging Terraform Deployments
Some common Terraform errors include authentication failures and state mismatches. Provider authentication issues can be resolved by verifying permissions and credentials. State mismatches often require manual state adjustments:
terraform state rm databricks_cluster.example
terraform apply
Quota restrictions and API rate limits must be handled by adjusting Terraform configurations to include retries and back-off mechanisms.
Databricks Deployments Optimization.
More resources like Unity Catalog for data governance can be managed with Terraform:
resource "databricks_metastore" "unity" {
name = "example-metastore"
}
MLflow tracking servers can also be set up to manage ML models and notebooks through Git integration, ensuring streamlined workflows.
This tutorial has shown how to automate Databricks infrastructure with Terraform, including infra provisioning, access control, job automation, CI/CD integration, and monitoring, but more optimizations like cost management, security best practices, and additional Databricks features can be included.
Pro tips:
1. Follow this guide to know how to manage secret scopes in databricks.
See more
James Sandy
James is interested in solving problems using data, AI, and cloud technology, and he researches AI safety.