If you’re new to Unstructured, read this note first.Before you can create a source connector, you must first sign in to your Unstructured account:After you sign in, the Unstructured user interface (UI) appears, which you use to get your Unstructured API key, as follows:
  1. After you sign in to your Unstructured Starter account, click API Keys on the sidebar.
     For a Team or Enterprise account, before you click API Keys, make sure you have selected the organizational workspace you want to create an API key for. Each API key works with one and only one organizational workspace. Learn more.
  2. Click Generate API Key.
  3. Follow the on-screen instructions to finish generating the key.
  4. Click the Copy icon next to your new key to add the key to your system’s clipboard. If you lose this key, simply return and click the Copy icon again.
After you create the source connector, add it along with a destination connector to a workflow. Then run the worklow as a job. To learn how, try out the hands-on Workflow Endpoint quickstart, go directly to the quickstart notebook, or watch the two 4-minute video tutorials for the Unstructured Python SDK.You can also create source connectors with the Unstructured user interface (UI). Learn how.If you need help, email Unstructured Support at support@unstructured.io.You are now ready to start creating a source connector! Keep reading to learn how.
Ingest your files into Unstructured from Databricks Volumes. The requirements are as follows.
  • A Databricks account on AWS, Azure, or GCP.
  • A workspace within the Databricks account for AWS, Azure, or GCP.
  • The workspace’s URL. Get the workspace URL for AWS, Azure, or GCP. Examples:
    • AWS: https://<workspace-id>.cloud.databricks.com
    • Azure: https://adb-<workspace-id>.<random-number>.azuredatabricks.net
    • GCP: https://<workspace-id>.<random-number>.gcp.databricks.com
    Do not add a trailing slash (/) to the workspace URL.
  • The Databricks authentication details. For more information, see the documentation for AWS, Azure, or GCP. For the Unstructured UI or the Unstructured API, the following Databricks authentication types are supported:
    • Databricks OAuth machine-to-machine (M2M) authentication for
      AWS, Azure, or GCP.       You will need the the Client ID (or UUID or Application ID) and OAuth Secret (client secret) values for the corresponding service principal. Note that for Azure, only Databricks managed service principals are supported. Microsoft Entra ID managed service principals are not supported. The following video shows how to create a Databricks managed service principal:
    • Databricks personal access token authentication for AWS, Azure, or GCP. You will need the personal access token’s value. The following video shows how to create a Databricks personal access token:
    For Unstructured Ingest, the following Databricks authentication types are supported:
    • For Databricks personal access token authentication for AWS, Azure, or GCP: The personal access token’s value. The following video shows how to create a Databricks personal access token:
    • For username and password (basic) authentication (AWS only): The user’s name and password values.
    • For OAuth machine-to-machine (M2M) authentication (AWS, Azure, and GCP): The client ID and OAuth secret values for the corresponding service principal.
    • For OAuth user-to-machine (U2M) authentication (AWS, Azure, and GCP): No additional values.
    • For Azure managed identities (formerly Managed Service Identities (MSI) authentication) (Azure only): The client ID value for the corresponding managed identity.
    • For Microsoft Entra ID service principal authentication (Azure only): The tenant ID, client ID, and client secret values for the corresponding service principal.
    • For Azure CLI authentication (Azure only): No additional values.
    • For Microsoft Entra ID user authentication (Azure only): The Entra ID token for the corresponding Entra ID user.
    • For Google Cloud Platform credentials authentication (GCP only): The local path to the corresponding Google Cloud service account’s credentials file.
    • For Google Cloud Platform ID authentication (GCP only): The Google Cloud service account’s email address.
  • The name of the parent catalog in Unity Catalog for AWS, Azure, or GCP for the volume.
  • The name of the parent schema (formerly known as a database) in Unity Catalog for AWS, Azure, or GCP for the volume.
  • The name of the volume in Unity Catalog for AWS, Azure, or GCP, and optionally any path in that volume that you want to access directly, beginning with the volume’s root.
  • The Databricks workspace user or service principal must have the following minimum set of privileges to read from or write to the existing volume in Unity Catalog:
    • USE CATALOG on the volume’s parent catalog in Unity Catalog.
    • USE SCHEMA on the volume’s parent schema (formerly known as a database) in Unity Catalog.
    • READ VOLUME and WRITE VOLUME on the volume.
    The following videos shows how to create and set privileges for a catalog, schema (formerly known as a database), and volume in Unity Catalog. Learn more about how to check and set Unity Catalog privileges for AWS, Azure, or GCP.
To create a Databricks Volumes source connector, see the following examples. 
import os

from unstructured_client import UnstructuredClient
from unstructured_client.models.operations import CreateSourceRequest
from unstructured_client.models.shared import CreateSourceConnector

with UnstructuredClient(api_key_auth=os.getenv("UNSTRUCTURED_API_KEY")) as client:
    response = client.sources.create_source(
        request=CreateSourceRequest(
            create_source_connector=CreateSourceConnector(
                name="<name>",
                type="databricks_volumes",
                config={
                    "catalog": "<catalog>",
                    "host": "<host>",
                    "schema_": "<schema>",
                    "volume": "<volume>",
                    "volume_path": "<volume_path>",

                    # For Databricks OAuth machine-to-machine (M2M) authentication:
                    # "client_id": "<client_id>",
                    # "client_secret": "<client_secret>",

                    # For Databricks personal access token authentication:
                    # "token": "<token>"
                }
            )
        )
    )

    print(response.source_connector_information)
Replace the preceding placeholders as follows:
  • <name> (required) - A unique name for this connector.
  • <host> (required) - The Databricks workspace host URL.
    Do not add a trailing slash (/) to the workspace host URL.
  • <client-id> (required) - For Databricks OAuth machine-to-machine (M2M) authentication, the Client ID (or UUID or Application ID) value for the Databricks managed service principal that has the appropriate privileges to the volume.
  • <client-secret> (required) - For Databricks OAuth M2M authentication, the associated OAuth Secret value for the Databricks managed service principal that has the appropriate privileges to the volume.
  • <token> (required) - For Databricks personal access token authentication, the personal access token’s value.
  • <catalog> (required) - The name of the catalog to use.
  • <schema> - The name of the associated schema. If not specified, default is used.
  • <volume> (required) - The name of the associated volume.
  • <volume_path> - Any optional path to access within the volume.