Use Unity Catalog service credentials to connect to external cloud services

This article describes how to use a service credential in Unity Catalog to connect to external cloud services. A service credential object in Unity Catalog encapsulates a long-term cloud credential that provides access to an external cloud service that users need to connect to from Azure Databricks.

See also:

Before you begin

Before you can use a service credential to connect to an external cloud service, you must have:

  • An Azure Databricks workspace that is enabled for Unity Catalog.

  • A compute resource that is on Databricks Runtime 16.2 or above.

    SQL warehouses are not supported for using service credentials directly. See Use a service credential in a Python UDF.

    The Public Preview version of service credentials is available on Databricks Runtime 15.4 LTS and above, with Python support but no Scala support.

  • A service credential created in your Unity Catalog metastore that gives access to the cloud service.

  • The ACCESS privilege on the service credential or ownership of the service credential.

Use a service credential in your code

This section provides examples of using service credentials in a notebook. Replace placeholder values. These examples don't necessarily show the installation of required libraries, which depend on the client service you want to access.

Only Python is supported.

Use a service credential in a Python UDF

In UDFs, use databricks.service_credentials.getServiceCredentialsProvider() to access service credentials. The dbutils.credentials.getServiceCredentialsProvider() function used in notebooks isn't available in UDF execution contexts.

Requirements depend on the type of UDF:

UDF type Requirements
PySpark session-scoped scalar UDF Databricks Runtime 17.1 or above on classic compute, or a serverless notebook or job session running environment version 3 or above.
Batch Unity Catalog Python UDF Databricks Runtime 16.3 or above on classic compute; serverless compute; pro and serverless SQL warehouses. Environment version 6 is not required.
Scalar Unity Catalog Python UDF Databricks Runtime 18.1 or above on classic compute. On serverless compute and on pro and serverless SQL warehouses, explicitly set the UDF's environment_version to 6 or above. Omitting the entire ENVIRONMENT clause or setting environment_version = 'None' does not enable this feature on these compute types. Classic compute does not require environment version 6.

To make outbound calls from a scalar or Batch Unity Catalog Python UDF on a serverless SQL warehouse, enable the Enable networking for isolated workloads in Serverless SQL Warehouses Public Preview on your workspace's Previews page. Pro SQL warehouses do not require this preview.

To create a Unity Catalog UDF that declares a service credential, the function creator must have ACCESS on the credential. On serverless compute, pro and serverless SQL warehouses, and standard access mode compute, callers need the usual function privileges, including EXECUTE, but do not need ACCESS on the credential. On dedicated access mode compute, callers must also have ACCESS.

PySpark session-scoped UDFs use the caller's permissions. The caller must have ACCESS on the service credential; on dedicated access mode compute, the caller must have MANAGE.

See environment versions and custom dependency requirements.

Python example: configure an Azure SDK client to use a specific service credential

from azure.keyvault.secrets import SecretClient # example Azure SDK client

credential = dbutils.credentials.getServiceCredentialsProvider('your-service-credential')
vault_url = "https://your-keyvault-name.vault.azure.net/"
client = SecretClient(vault_url=vault_url, credential=credential)

Specify a default service credential for a compute resource

You can optionally specify a default service credential for an all-purpose or jobs compute cluster by setting an environment variable. By default, the SDK uses that service credential if no authentication is provided. Users still require ACCESS on that service credential to connect to the external cloud service. Databricks does not recommend this approach, because it makes your code less portable than naming the service credential in your code.

Note

Serverless compute and SQL warehouses don't support environment variables, and therefore they don't support default service credentials.

  1. Open the edit page for the cluster.

    See Manage classic compute.

  2. Click Advanced at the bottom of the page and go to the Spark tab.

  3. Add the following entry in Environment variables, replacing <your-service-credential>:

    DATABRICKS_DEFAULT_SERVICE_CREDENTIAL_NAME=<your-service-credential>

The following code samples do not specify a service credential. Instead, they use the service credential specified in the DATABRICKS_DEFAULT_SERVICE_CREDENTIAL_NAME environment variable:

Python

If you are using a default service credential, you don't need to specify credentials as an argument:

from azure.identity import DefaultAzureCredential
from azure.keyvault.secrets import SecretClient

credential = DefaultAzureCredential()
vault_url = "https://your-keyvault-name.vault.azure.net/"
client = SecretClient(vault_url=vault_url, credential=credential)

Compare this to the example in Python example: configure an Azure SDK client to use a specific service credential, which does not import DefaultAzureCredential and adds the credential specification:

credential = dbutils.credentials.getServiceCredentialsProvider('your-service-credential')
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