notebook.md

subcategory	Workspace

databricks_notebook Resource

This resource allows you to manage Databricks Notebooks. You can also work with databricks_notebook and databricks_notebook_paths data sources.

-> This resource can only be used with a workspace-level provider!

Example Usage

You can declare Terraform-managed notebook by specifying source attribute of corresponding local file. Only .scala, .py, .sql, .r, and .ipynb extensions are supported, if you would like to omit the language attribute.

data "databricks_current_user" "me" {
}

resource "databricks_notebook" "ddl" {
  source = "${path.module}/DDLgen.py"
  path   = "${data.databricks_current_user.me.home}/AA/BB/CC"
}

You can also create a managed notebook with inline sources through content_base64 and language attributes.

resource "databricks_notebook" "notebook" {
  content_base64 = base64encode(<<-EOT
    # created from ${abspath(path.module)}
    display(spark.range(10))
    EOT
  )
  path     = "/Shared/Demo"
  language = "PYTHON"
}

You can also manage Databricks Archives to import the whole folders of notebooks statically. Whenever you update the .dbc file, the Terraform-managed notebook folder is removed and replaced with contents of the new .dbc file. You are strongly advised to use .dbc format only with source attribute of the resource:

resource "databricks_notebook" "lesson" {
  source = "${path.module}/IntroNotebooks.dbc"
  path   = "/Shared/Intro"
}

Argument Reference

-> Notebook on Databricks workspace would only be changed, if Terraform stage did change. This means that any manual changes to managed notebook won't be overwritten by Terraform, if there's no local change to notebook sources. Notebooks are identified by their path, so changing notebook's name manually on the workspace and then applying Terraform state would result in creation of notebook from Terraform state.

The size of a notebook source code must not exceed a few megabytes. The following arguments are supported:

• path - (Required) The absolute path of the notebook or directory, beginning with "/", e.g. "/Demo".
• source - Path to notebook in source code format on local filesystem. Conflicts with content_base64.
• content_base64 - The base64-encoded notebook source code. Conflicts with source. Use of content_base64 is discouraged, as it's increasing memory footprint of Terraform state and should only be used in exceptional circumstances, like creating a notebook with configuration properties for a data pipeline.
• language - (required with content_base64) One of SCALA, PYTHON, SQL, R.
• provider_config - (Optional) Configure the provider for management through account provider. This block consists of the following fields:
  • workspace_id - (Required) Workspace ID which the resource belongs to. This workspace must be part of the account which the provider is configured with.

Attribute Reference

In addition to all arguments above, the following attributes are exported:

• id - Path of notebook on workspace
• url - Routable URL of the notebook
• object_id - Unique identifier for a NOTEBOOK
• workspace_path - path on Workspace File System (WSFS) in form of /Workspace + path

Access Control

• databricks_permissions can control which groups or individual users can access notebooks or folders.

Import

The resource notebook can be imported using notebook path

import {
  to = databricks_notebook.this
  id = "/path/to/notebook"
}

Alternatively, when using terraform version 1.4 or earlier, import using the terraform import command:

terraform import databricks_notebook.this /path/to/notebook

Related Resources

The following resources are often used in the same context:

• End to end workspace management guide.
• databricks_cluster to create Databricks Clusters.
• databricks_directory to manage directories in Databricks Workpace.
• databricks_job to manage Databricks Jobs to run non-interactive code in a databricks_cluster.
• databricks_notebook data to export a notebook from Databricks Workspace.
• databricks_notebook_paths data to list notebooks in Databricks Workspace.
• databricks_pipeline to deploy Lakeflow Declarative Pipelines.
• databricks_repo to manage Databricks Repos.
• databricks_secret to manage secrets in Databricks workspace.
• databricks_secret_acl to manage access to secrets in Databricks workspace.
• databricks_secret_scope to create secret scopes in Databricks workspace.
• databricks_user to manage users, that could be added to databricks_group within the workspace.
• databricks_user data to retrieve information about databricks_user.