Azure Blob

Connect to your Azure Blob containers.

Getting Started

Prerequisites to create an Azure Blob based workflow. You will need

1. A connection to Azure Blob.

2. A source container.

3. A destination container. This can be the same as your source container.

Configuring an Azure Blob Connection

Azure Blob related actions require creating an azure connection. The connection must be configured with the correct permissions for each Gretel Action.

For specific permissions, please refer to the Minimum Permissions section under each corresponding action.

There are three ways to authenticate a Gretel Azure Blob Connection, each method requires different fields for connection creation:

1. Account Access Key

2. #entra-id-account-active-directory

3. SAS Token

Account Access Key

Connection Creation Parameters

name	Display name of your choosing used to identify your connection within Gretel.
account_name	Name of the Storage Account.
access_key	Storage Account Access Key.
default_container	Default container to crawl data from. Different containers can be chosen at the azure_source and azure_destination actions.

CLI

First, create a file on your local computer containing the connection credentials. This file should also include type, name , config, and credentials. connection_target_type is optional; if omitted, the connection can be used for both source and destination action. The config and credentials fields should contain fields that are specific to the connection being created.

Below is an example Azure Blob connection using access key credentials:

{
    "type": "azure",
    "name": "my-azure-connection",
    "connection_target_type": "source"
    "config": {
        "account_name": "mystorageaccount",
        "default_container": "mycontainer",
    },
    "credentials": {
        "access_key": "..."
    }
}

Now that you've created the credentials file, use the CLI to create the connection

gretel connections create --project [project id] --from-file [credential_file.json]

Console

• Navigate to the Connections page using the menu item in the left sidebar.
• Click the New Connection button.

• Step 1, choose the Type for the Connection - Azure Blob.

• Step 2, choose the Project for your Connection.

• Step 3, fill in the credentials and select Add Connection.

SDK

from gretel_client import create_or_get_unique_project
from gretel_client.config import get_session_config
from gretel_client.rest_v1.api.connections_api import ConnectionsApi
from gretel_client.rest_v1.models import (
    CreateConnectionRequest,
    UpdateConnectionRequest,
)

session = get_session_config()
connection_api = session.get_v1_api(ConnectionsApi)

project = create_or_get_unique_project(name="azure-workflow")

connection = connection_api.create_connection(
    CreateConnectionRequest(
        name="my-azure-connection",
        project_id=project.project_guid,
        type="azure",
        #connection_target_type="",
        config={
            "account_name": "mystorageaccount",
            "default_container": "mycontainer",
        },
        # note: best practice is to read in credentials from a file
        # or secret instead of directly embedding sensitive values
        # in python code.
        credentials={
            "access_key": "...",
        },
    )
)

Entra ID

name	Display name of your choosing used to identify your connection within Gretel.
account_name	Name of the Storage Account.
client_id	Application (client) ID.
tenant_id	Directory (tenant) ID.
username	Email of the Service Account.
entra_password	Password of the Service Account.
default_container	Default container to crawl data from. Different containers can be chosen at the azure_source and azure_destination actions.

CLI

First, create a file on your local computer containing the connection credentials. This file should also include type, name , config, and credentials. connection_target_type is optional; if omitted, the connection can be used for both source and destination action. The config and credentials fields should contain fields that are specific to the connection being created.

Below is an example Azure Blob connection using access key credentials:

{
    "type": "azure",
    "name": "my-azure-connection",
    "connection_target_type": "source"
    "config": {
        "account_name": "mystorageaccount",
        "default_container": "mycontainer",
        "entra_config": {
            "client_id": "12a345b6-1a23-1ab2-abc1-1ab234cde56f",
            "tenant_id": "78g901h2-7g89-7gh8-ghi7-7gh890ijk12l",
            "username": "[e-mail]",
    },
    "credentials": {
        "entra_password": "..."
    }
}

Now that you've created the credentials file, use the CLI to create the connection

gretel connections create --project [project id] --from-file [credential_file.json]

Console

Console support for creating Azure Blob connections using Entra ID is coming soon. For now, you can create connections using Entra ID via CLI or SDK and then use those connections in Console.

SDK

from gretel_client import create_or_get_unique_project
from gretel_client.config import get_session_config
from gretel_client.rest_v1.api.connections_api import ConnectionsApi
from gretel_client.rest_v1.models import (
    CreateConnectionRequest,
    UpdateConnectionRequest,
)

session = get_session_config()
connection_api = session.get_v1_api(ConnectionsApi)

project = create_or_get_unique_project(name="azure-workflow")

connection = connection_api.create_connection(
    CreateConnectionRequest(
        name="my-azure-connection",
        project_id=project.project_guid,
        type="azure",
        #connection_target_type="",
        config={
            "account_name": "mystorageaccount",
            "default_container": "mycontainer",
            "entra_config": {
                "client_id": "12a345b6-1a23-1ab2-abc1-1ab234cde56f",
                "tenant_id": "78g901h2-7g89-7gh8-ghi7-7gh890ijk12l",
                "username": "[e-mail]",
            },
        },
        # note: best practice is to read in credentials from a file
        # or secret instead of directly embedding sensitive values
        # in python code.
        credentials={
            "entra_password": "...",
        },
    )
)

SAS Token

name	Display name of your choosing used to identify your connection within Gretel.
account_name	Name of the Storage Account.
sas_token	Shared Access Signatures.
default_container	Default container to crawl data from. Different containers can be chosen at the azure_source and azure_destination actions.

CLI

First, create a file on your local computer containing the connection credentials. This file should also include type, name , config, and credentials. connection_target_type is optional; if omitted, the connection can be used for both source and destination action. The config and credentials fields should contain fields that are specific to the connection being created.

Below is an example Azure Blob connection file using access key credentials:

{
    "type": "azure",
    "name": "my-azure-connection",
    "connection_target_type": "source"
    "config": {
        "account_name": "mystorageaccount",
        "default_container": "mycontainer",
    },
    "credentials": {
        "sas_token": "..."
    }
}

Now that you've created the credentials file, use the CLI to create the connection

gretel connections create --project [project id] --from-file [credential_file.json]

Console

Console support for creating Azure Blob connections using SAS Tokens is coming soon. For now, you can create connections using SAS Tokens via CLI or SDK and then use those connections in Console.

SDK

from gretel_client import create_or_get_unique_project
from gretel_client.config import get_session_config
from gretel_client.rest_v1.api.connections_api import ConnectionsApi
from gretel_client.rest_v1.models import (
    CreateConnectionRequest,
    UpdateConnectionRequest,
)

session = get_session_config()
connection_api = session.get_v1_api(ConnectionsApi)

project = create_or_get_unique_project(name="azure-workflow")

connection = connection_api.create_connection(
    CreateConnectionRequest(
        name="my-azure-connection",
        project_id=project.project_guid,
        type="azure",
        #connection_target_type="",
        config={
            "account_name": "mystorageaccount",
            "default_container": "mycontainer",
        },
        # note: best practice is to read in credentials from a file
        # or secret instead of directly embedding sensitive values
        # in python code.
        credentials={
            "sas_token": "...",
        },
    )
)