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Showing content from https://docs.databricks.com/aws/en/security/secrets/example-secret-workflow below:

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.

Create a secret scope called jdbc.

Bash

databricks secrets create-scope jdbc

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

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
• Scala

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()
)

Scala

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

val 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.

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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