Select your cookie preferences

We use essential cookies and similar tools that are necessary to provide our site and services. We use performance cookies to collect anonymous statistics, so we can understand how customers use our site and make improvements. Essential cookies cannot be deactivated, but you can choose “Customize” or “Decline” to decline performance cookies.

If you agree, AWS and approved third parties will also use cookies to provide useful site features, remember your preferences, and display relevant content, including relevant advertising. To accept or decline all non-essential cookies, choose “Accept” or “Decline.” To make more detailed choices, choose “Customize.”

Create user-defined AWS Glue connections

Focus mode
Create user-defined AWS Glue connections - Amazon SageMaker AI
Note

All AWS Glue connections created by users via the SQL extension UI are automatically tagged with the following:

  • UserProfile: user-profile-name

  • AppType: "JL"

Those tags applied to the AWS Glue connections created via the SQL extension UI serve two purposes. The "UserProfile": user-profile-name tag allows the identification of the specific user profile that created the AWS Glue connection, providing visibility into the user responsible for the connection. The "AppType": "JL" tag categorizes the provenance of the connection, associating it with the JupyterLab application. This allows these connections to be differentiated from those that may have been created through other means, such as the AWS CLI.

Prerequisites

Before creating a AWS Glue connection using the SQL extension UI, ensure that you have completed the following tasks:

User workflow

The following steps provide the user workflow when creating user connections:

  1. Select the data source type: Upon choosing the Add new connection icon, a form opens, prompting the user to select the type of data source they want to connect to, such as Amazon Redshift, Athena, or Snowflake.

  2. Provide connection properties: Based on the selected data source, the relevant connection properties are dynamically loaded. The form indicates which fields are mandatory or optional for the chosen data source. To learn about the available properties for your data source, see Connection parameters.

  3. Select your AWS Secrets Manager ARN: For Amazon Redshift and Snowflake data sources, the user is prompted to select the AWS Secrets Manager ARN that stores sensitive information such as the username and password. To learn about the creation of a secret for your data source, see Create secrets for database access credentials in Secrets Manager.

  4. Save your connection details: Upon clicking Create, the provided connection properties are saved as a AWS Glue connection.

  5. Test your connection: If the connection is successful, the associated databases and tables become visible in the explorer. If the connection fails, an error message is displayed, prompting the user to review and correct the connection details.

  6. Familiarize with SQL extension features: To learn about the capabilities of the extension, see the SQL extension features and usage.

  7. (Optional) Update or delete user-created connections: Provided that the user has been granted the necessary permissions, they can update or delete the connections they have created. To learn more about the required permissions, see User-defined connections required IAM permissions.

PrivacySite termsCookie preferences
© 2025, Amazon Web Services, Inc. or its affiliates. All rights reserved.