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Secret management

  • Artikkeli

Sometimes accessing data requires that you authenticate to external data sources through JDBC. Instead of directly entering your credentials into a notebook, you can use Azure Databricks secrets to store your credentials and reference them in notebooks and jobs. This article provides an overview of Azure Databricks secrets.

Secrets overview

To configure and use secrets you:

  1. Create a secret scope. A secret scope is collection of secrets identified by a name.
  2. Add secrets to the scope
  3. Assign permissions on the secret scope.
  4. Access secrets using Databricks Utilities, see Secrets utility (dbutils.secrets).

For an end-to-end example of how to use secrets in your workflows, see Tutorial: Create and use a Databricks secret. To use a secret in a Spark configuration property or environment variable, see Use a secret in a Spark configuration property or environment variable.

Warning

Administrators, secret creators, and users granted permission can read Azure Databricks secrets. While Databricks makes an effort to redact secret values that might be displayed in notebooks, it is not possible to prevent such users from reading secrets. See Secret redaction.

Manage secret scopes

A secret scope is collection of secrets identified by a name. Databricks recommends aligning secret scopes to roles or applications rather than individuals.

There are two types of secret scope:

  • Azure Key Vault-backed: You can reference secrets stored in an Azure Key Vault using Azure Key Vault-backed secret scopes. Azure Key Vault-backed secret scope is a read-only interface to the Key Vault. You must manage secrets in Azure Key Vault-backed secret scopes in Azure.
  • Databricks-backed: A Databricks-backed secret scope is stored in an encrypted database owned and managed by Azure Databricks.

After creating a secret scope, you can assign permissions to grant users access to read, write, and manage scret scopes.

Create an Azure Key Vault-backed secret scope

This section describes how to create an Azure Key Vault-backed secret scope using the Azure portal and the Azure Databricks workspace UI. You can also create an Azure Key Vault-backed secret scope using the Databricks CLI.

Requirements

  • You must have an Azure key vault instance. If you do not have a key vault instance, follow the instructions in Create a Key Vault using the Azure portal.
  • You must have the Key Vault Contributor, Contributor, or Owner role on the Azure key vault instance that you want to use to back the secret scope.

Note

Creating an Azure Key Vault-backed secret scope requires the Contributor or Owner role on the Azure key vault instance even if the Azure Databricks service has previously been granted access to the key vault.

If the key vault exists in a different tenant than the Azure Databricks workspace, the Azure AD user who creates the secret scope must have permission to create service principals in the key vault’s tenant. Otherwise, the following error occurs:

Unable to grant read/list permission to Databricks service principal to KeyVault 'https://xxxxx.vault.azure.net/': Status code 403, {"odata.error":{"code":"Authorization_RequestDenied","message":{"lang":"en","value":"Insufficient privileges to complete the operation."},"requestId":"XXXXX","date":"YYYY-MM-DDTHH:MM:SS"}}

Configure your Azure key vault instance for Azure Databricks

  1. Log in to the Azure Portal, find and select the Azure key vault instance.

  2. Under Settings, click the Access configuration tab.

  3. Set Permission model to Vault access policy.

    Note

    Creating an Azure Key Vault-backed secret scope role grants the Get and List permissions to the application ID for the Azure Databricks service using key vault access policies. The Azure role-based access control permission model is not supported with Azure Databricks.

  4. Under Settings, select Networking.

  5. In Firewalls and virtual networks set Allow access from: to Allow public access from specific virtual networks and IP addresses.

    Under Exception, check Allow trusted Microsoft services to bypass this firewall.

    Note

    You can also set Allow access from: to Allow public access from all networks.

Create an Azure Key Vault-backed secret scope

  1. Go to https://<databricks-instance>#secrets/createScope. Replace <databricks-instance> with the workspace URL of your Azure Databricks deployment. This URL is case sensitive. For example, scope in createScope must use an uppercase S).

    Create scope

  2. Enter the name of the secret scope. Secret scope names are case insensitive.

  3. In Manage Principal select Creator or All workspace users to specify which users have the MANAGE permission on the secret scope.

    The MANAGE permission allows users to read, write, and grant permissions on the scope. Your account must have the Premium plan to choose Creator.

  4. Enter the DNS Name (for example, https://databrickskv.vault.azure.net/) and Resource ID, for example:

    /subscriptions/xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx/resourcegroups/databricks-rg/providers/Microsoft.KeyVault/vaults/databricksKV

    These properties are available from the Settings > Properties tab of an Azure Key Vault in your Azure portal.

  5. Click Create.

  6. Use the Databricks CLI databricks secrets list-scopes command to verify that the scope was created successfully.

Create a Databricks-backed secret scope

This section describes how to create a secret scope using the What is the Databricks CLI? (version 0.205 and above). You can also use the Secrets API.

Secret scope names:

  • Must be unique within a workspace.
  • Must consist of alphanumeric characters, dashes, underscores, @, and periods, and can not exceed 128 characters.
  • Are case insensitive.

Secret scope names are considered non-sensitive and are readable by all users in the workspace.

To create a scope using the Databricks CLI:

databricks secrets create-scope <scope-name>

By default, scopes are created with MANAGE permission for the user who created the scope. After you have created a Databricks-backed secret scope, you can add secrets to it.

List secret scopes

To list the existing scopes in a workspace using the CLI:

databricks secrets list-scopes

You can also list secret scopes using the Secrets API.

Delete a secret scope

Deleting a secret scope deletes all secrets and ACLs applied to the scope. To delete a scope using the CLI, run the following:

databricks secrets delete-scope <scope-name>

You can also delete a secret scope using the Secrets API.

Manage secrets

A secret is a key-value pair that stores sensitive material using a key name that is unique within a secret scope.

This section describes how to create a secret scope using the What is the Databricks CLI? (version 0.205 and above). You can also use the Secrets API. Secret names are case insensitive.

Create a secret

The method for creating a secret depends on whether you are using an Azure Key Vault-backed scope or a Databricks-backed scope.

Create a secret in an Azure Key Vault-backed scope

To create a secret in Azure Key Vault you use the Azure portal or Azure Set Secret REST API. For an example, see Step 4: Add the client secret to Azure Key Vault.

Azure Key Vault

Create a secret in a Databricks-backed scope

This section describes how to create a secret using the What is the Databricks CLI? (version 0.205 and above) or in a notebook using the Databricks SDK for Python. You can also use the Secrets API. Secret names are case insensitive.

Databricks CLI

When you create a secret in a Databricks-backed scope, you can specify the secret value in one of three ways:

  • Specify the value as a string using the –string-value flag.
  • Input the secret when prompted interactively (single-line secrets).
  • Pass the secret using standard input (multi-line secrets).

For example:

databricks secrets put-secret --json '{
  "scope": "<scope-name>",
  "key": "<key-name>",
  "string_value": "<secret>"
}'

If you are creating a multi-line secret, you can pass the secret using standard input. For example:

(cat << EOF
this
is
a
multi
line
secret
EOF
) | databricks secrets put-secret <scope-name> <key-name>

Databricks SDK for Python

from databricks.sdk import WorkspaceClient

w = WorkspaceClient()

w.secrets.put_secret("<secret_scope>","<key-name>",string_value ="<secret>")

Read a secret

To read a secret in a notebook or job you must use the Secrets utility (dbutils.secrets). For example:

password = dbutils.secrets.get(scope = "<scope-name>", key = "<key-name>")

List secrets

To list secrets in a given scope:

databricks secrets list-secrets <scope-name>

The response displays metadata information about the secrets, such as the secrets’ key names. You use the Secrets utility (dbutils.secrets) in a notebook or job to list this metadata. For example:

dbutils.secrets.list('my-scope')

Delete a secret

To delete a secret from a scope with the Databricks CLI:

databricks secrets delete-secret <scope-name> <key-name>

You can also use the Secrets API.

To delete a secret from a scope backed by Azure Key Vault, use the Azure SetSecret REST API or Azure portal UI.

Manage secret scope permissions

By default, the user that creates the secret scopes is granted the MANAGE permission. This allows the scope creator to read secrets in the scope, write secrets to the scope, and manage permissions on the scope.

Note

Secret ACLs are at the scope level. If you use Azure Key Vault-backed scopes, users that are granted access to the scope have access to all secrets in the Azure Key Vault. To restrict access, use separate Azure key vault instances.

This section describes how to manage secret access control using the What is the Databricks CLI? (version 0.205 and above). You can also use the Secrets API. For secret permission levels, see Secret ACLs

Grant a user permissions on a secret scope

To grant a user permissions on a secret scope using the Databricks CLI:

databricks secrets put-acl <scope-name> <principal> <permission>

Making a put request for a principal that already has an applied permission overwrites the existing permission level.

The principal field specifies an existing Azure Databricks principal. A user is specified using their email address, a service principal using its applicationId value, and a group using its group name. For more information, see Principal.

View secret scope permissions

To view all secret scope permissions for a given secret scope:

databricks secrets list-acls <scope-name>

To get the secret scope permissions applied to a principal for a given secret scope:

databricks secrets get-acl <scope-name> <principal>

If no ACL exists for the given principal and scope, this request fails.

Delete a secret scope permission

To delete a secret scope permission applied to a principal for a given secret scope:

databricks secrets delete-acl <scope-name> <principal>

Secret redaction

Storing credentials as Azure Databricks secrets makes it easy to protect your credentials when you run notebooks and jobs. However, it is easy to accidentally print a secret to standard output buffers or display the value during variable assignment.

To prevent this, Azure Databricks redacts all secret values that are read using dbutils.secrets.get(). When displayed in notebook cell output, the secret values are replaced with [REDACTED].

For example, if you set a variable to a secret value using dbutils.secrets.get() and then print that variable, that variable is replaced with [REDACTED].

Warning

Secret redaction for notebook cell output applies only to literals. The secret redaction functionality does not prevent deliberate and arbitrary transformations of a secret literal. To ensure the proper control of secrets, you should use access control lists to limit permissions to run commands. This prevents unauthorized access to shared notebook contexts.