Databricks Provider

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Use the Databricks Terraform provider to interact with almost all of Databricks resources.

Compute resources

• Deploy databricks_cluster on selected databricks_node_type

• Schedule automated databricks_job

• Control cost and data access with databricks_cluster_policy

• Speedup job & cluster startup with databricks_instance_pool

• Customize clusters with databricks_global_init_script

• Manage few databricks_notebook, and even list them

• Manage databricks_repo

Storage

• Manage JAR, Wheel & Egg libraries through databricks_dbfs_file

• List entries on DBFS with databricks_dbfs_file_paths data source

• Get contents of small files with databricks_dbfs_file data source

• Mount storage with databricks_mount resource

Security

• Organize databricks_user into databricks_group through databricks_group_member, also reading metadata

• Create databricks_service_principal with databricks_obo_token to enable even more restricted access control.

• Create databricks_service_principal with databricks_service_principal_secret to authenticate with the service principal OAuth tokens (Only for AWS deployments)

• Manage data access with databricks_instance_profile, which can be assigned through databricks_group_instance_profile and databricks_user_instance_profile

• Control which networks can access workspace with databricks_ip_access_list

• Generically manage databricks_permissions

• Manage data object access control lists with databricks_sql_permissions

• Keep sensitive elements like passwords in databricks_secret, grouped into databricks_secret_scope and controlled by databricks_secret_acl

Databricks workspace on AWS

• Create workspaces in your VPC with DBFS using cross-account IAM roles, having your notebooks encrypted with CMK.
• Use predefined AWS IAM Policy Templates: databricks_aws_assume_role_policy, databricks_aws_crossaccount_policy, databricks_aws_bucket_policy
• Configure billing and audit databricks_mws_log_delivery

Databricks SQL

• Create databricks_sql_endpoint controlled by databricks_permissions.
• Manage queries and their visualizations.
• Manage dashboards and their widgets.
• Provide global configuration for all SQL warehouses

Machine Learning

• Create models in Unity Catalog.
• Create MLflow experiments.
• Create models in the workspace model registry.
• Create model serving endpoints.

Want to learn more about Databricks + Resourcely? Get in touch.