Create an Azure connection to an Edge or Collibra Cloud site

Do you use a vault?

You can use a vault to add your data source information to your Edge site connection. Check the connection property table below to see which information is available for your vault. Vaults are not available for Collibra Cloud sites.	No vault AWS Secrets Manager Azure Key Vault CyberArk Vault Google Secret Manager HashiCorp Vault
	Select another vault I don't use a vault AWS Secrets Manager Azure Key Vault CyberArk Vault Google Secret Manager HashiCorp Vault
How to use your vault... To use your vault, do the following: In the Value Type field, select Vault Key. Enter the query value to identify the secret in your vault. Example  Note The Query must be a string containing the properties required to identify the secret. Each property must be separated by a semicolon (;). For example: Safe=<SafeName>;Folder=<FolderName>;Object=<ObjectName> If a property is a folder with sub-folders, use a backslash (\) to define the folder path. For example: Folder=Root\Top Secrets\More Secrets For more information about query formats and supported properties, go to the CyberArk Credential Provider documentation. To use your vault, do the following: In the Value Type field, select Vault Key. Enter the required information: Secret Engine Type Select one of the following: Key Value Database Engine Path The engine path to your vault where the value is stored. Secret Path The secret path to your vault where the value is stored. Field If your Secret Engine Type is Key Value, enter the name of the field to your vault where the value is stored. Role If your Secret Engine Type is Database, enter the role specified in the Database engine. Example  To use your vault, do the following: In the Value Type field, select Vault Key. Enter the required information: Vault Name The name of your Azure Key Vault in your Azure Key Vault service where the value is stored. Secret Name The name of the secret in your vault where the value is stored. Example  To use your vault, do the following: In the Value Type field, select Vault Key. Enter the required information: Secret Name The name of the secret in your vault where the value is stored. Field If the secret stored in your AWS Secrets Manager is a JSON value, for example {"pass1": "my-password", "pass2": "my-password2"}, then you need to specify the Field to point to the exact JSON value that should be used. For example, Secret Name: edge-db-customer; Field: pass. Note If the secret stored in your AWS Secrets Manager is a plain string value, for example my-password, then you do not need to specify the Field. Example  To use your vault, do the following: In the Value Type field, select Vault Key. Enter the name of the secret in your vault where the value is stored. Example

Prerequisites

In your Collibra environment

• You either created and installed an Edge site or were granted a Collibra Cloud site.
  Note 

  If you have defined an outbound (forward) proxy on your Edge site, the integration will take that configuration into account when connecting to the AI platform. The following proxies are supported, using either a HTTP or HTTPS proxy URL:

  • Pass through (No authentication)
  • Pass through (Basic authentication)
  • No proxy for noProxy hosts defined by Edge

  This integration is not supported on Edge sites installed using a Man-in-the-middle (MITM) proxy.

• You have created and installed an Edge site.
• You have added a vault to your Edge site.
• You have a resource role with the Configure external system resource permission, for example, Owner.
• You have a global role with the Catalog global permission, for example, Catalog Author.
• You have a global role with the View Edge connections and capabilities global permission, for example, Edge integration engineer.

In your Azure ML environment

To integrate Azure ML assets, the service principal must have the following permissions on the Azure ML workspace:

• Microsoft.MachineLearningServices/workspaces/read
• Microsoft.MachineLearningServices/workspaces/models/read
• Microsoft.MachineLearningServices/workspaces/models/versions/read
• Microsoft.MachineLearningServices/workspaces/metadata/artifacts/read
• Microsoft.MachineLearningServices/workspaces/experiments/runs/read

These permissions are included in the built-in AzureML Data Scientist role. The built-in Reader role also includes them and synchronizes all models, including metrics. For more information on role assignments, go to the Azure ML access control documentation. A missing permission can produce a synchronization that shows a status of Completed with models missing and no error reported.