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Tutorial: Create and use a Databricks secret

In this tutorial, you use Databricks secrets to set up JDBC credentials for connecting to an Azure Data Lake Storage account.

Step 1: Create a secret scope​

Create a secret scope called jdbc.

Bash
databricks secrets create-scope jdbc

Step 2: Add secrets to the secret scope​

Add the secrets username and password. Run the following commands and enter the secret values in the opened editor.

Bash
databricks secrets put-secret jdbc username
databricks secrets put-secret jdbc password

Step 3: Use the secrets in a notebook​

Use the dbutils.secrets utility to access secrets in notebooks.

The following example reads the secrets that are stored in the secret scope jdbc to configure a JDBC read operation:

Python
username = dbutils.secrets.get(scope = "jdbc", key = "username")
password = dbutils.secrets.get(scope = "jdbc", key = "password")

df = (spark.read
  .format("jdbc")
  .option("url", "<jdbc-url>")
  .option("dbtable", "<table-name>")
  .option("user", username)
  .option("password", password)
  .load()
)

The values fetched from the scope are redacted from the notebook output. See Secret redaction.

Step 4: Grant a group permissions on the secret scope​

note

This step requires the Premium plan or above.

After verifying that the credentials were configured correctly, you can grant permissions on the secret scope to other users and groups in your workspace.

Grant the datascience group the READ permission to the secret scope:

Bash
databricks secrets put-acl jdbc datascience READ

For more information about secret access control, see Secret ACLs.

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