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Configure dataflow endpoints for Azure Data Lake Storage Gen2

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Important

Azure IoT Operations Preview – enabled by Azure Arc is currently in preview. You shouldn't use this preview software in production environments.

You'll need to deploy a new Azure IoT Operations installation when a generally available release is made available. You won't be able to upgrade a preview installation.

See the Supplemental Terms of Use for Microsoft Azure Previews for legal terms that apply to Azure features that are in beta, preview, or otherwise not yet released into general availability.

To send data to Azure Data Lake Storage Gen2 in Azure IoT Operations Preview, you can configure a dataflow endpoint. This configuration allows you to specify the destination endpoint, authentication method, table, and other settings.

Prerequisites

Create an Azure Data Lake Storage Gen2 dataflow endpoint

To configure a dataflow endpoint for Azure Data Lake Storage Gen2, we suggest using the managed identity of the Azure Arc-enabled Kubernetes cluster. This approach is secure and eliminates the need for secret management. Alternatively, you can authenticate with the storage account using an access token. When using an access token, you would need to create a Kubernetes secret containing the SAS token.

Use managed identity authentication

First, in Azure portal, go to the Arc-connected Kubernetes cluster and select Settings > Extensions. In the extension list, find the name of your Azure IoT Operations extension. Copy the name of the extension.

Then, assign a role to the managed identity that grants permission to write to the storage account, such as Storage Blob Data Contributor. To learn more, see Authorize access to blobs using Microsoft Entra ID.

Finally, create the DataflowEndpoint resource and specify the managed identity authentication method. Replace the placeholder values like <ENDPOINT_NAME> with your own.

Create a Kubernetes manifest .yaml file with the following content.

apiVersion: connectivity.iotoperations.azure.com/v1beta1
kind: DataflowEndpoint
metadata:
  name: <ENDPOINT_NAME>
  namespace: azure-iot-operations
spec:
  endpointType: DataLakeStorage
  dataLakeStorageSettings:
    host: https://<ACCOUNT>.blob.core.windows.net
    authentication:
      method: SystemAssignedManagedIdentity
      systemAssignedManagedIdentitySettings: {}

Then apply the manifest file to the Kubernetes cluster.

kubectl apply -f <FILE>.yaml

If you need to override the system-assigned managed identity audience, see the System-assigned managed identity section.

Use access token authentication

Follow the steps in the access token section to get a SAS token for the storage account and store it in a Kubernetes secret.

Then, create the DataflowEndpoint resource and specify the access token authentication method. Here, replace <SAS_SECRET_NAME> with name of the secret containing the SAS token as well as other placeholder values.

Create a Kubernetes manifest .yaml file with the following content.

apiVersion: connectivity.iotoperations.azure.com/v1beta1
kind: DataflowEndpoint
metadata:
  name: <ENDPOINT_NAME>
  namespace: azure-iot-operations
spec:
  endpointType: DataLakeStorage
  dataLakeStorageSettings:
    host: https://<ACCOUNT>.blob.core.windows.net
    authentication:
      method: AccessToken
      accessTokenSettings:
        secretRef: <SAS_SECRET_NAME>

Then apply the manifest file to the Kubernetes cluster.

kubectl apply -f <FILE>.yaml

Available authentication methods

The following authentication methods are available for Azure Data Lake Storage Gen2 endpoints.

For more information about enabling secure settings by configuring an Azure Key Vault and enabling workload identities, see Enable secure settings in Azure IoT Operations Preview deployment.

System-assigned managed identity

Using the system-assigned managed identity is the recommended authentication method for Azure IoT Operations. Azure IoT Operations creates the managed identity automatically and assigns it to the Azure Arc-enabled Kubernetes cluster. It eliminates the need for secret management and allows for seamless authentication with the Azure Data Lake Storage Gen2 account.

Before creating the dataflow endpoint, assign a role to the managed identity that has write permission to the storage account. For example, you can assign the Storage Blob Data Contributor role. To learn more about assigning roles to blobs, see Authorize access to blobs using Microsoft Entra ID.

To use system-assigned managed identity, specify the managed identity authentication method in the DataflowEndpoint resource. In most cases, you don't need to specify other settings. Not specifying an audience creates a managed identity with the default audience scoped to your storage account.

dataLakeStorageSettings:
  authentication:
    method: SystemAssignedManagedIdentity
    systemAssignedManagedIdentitySettings: {}

If you need to override the system-assigned managed identity audience, you can specify the audience setting.

dataLakeStorageSettings:
  authentication:
    method: SystemAssignedManagedIdentity
    systemAssignedManagedIdentitySettings:
      audience: https://<ACCOUNT>.blob.core.windows.net

Access token

Using an access token is an alternative authentication method. This method requires you to create a Kubernetes secret with the SAS token and reference the secret in the DataflowEndpoint resource.

Get a SAS token for an Azure Data Lake Storage Gen2 (ADLSv2) account. For example, use the Azure portal to browse to your storage account. On the left menu, choose Security + networking > Shared access signature. Use the following table to set the required permissions.

Parameter Enabled setting
Allowed services Blob
Allowed resource types Object, Container
Allowed permissions Read, Write, Delete, List, Create

To enhance security and follow the principle of least privilege, you can generate a SAS token for a specific container. To prevent authentication errors, ensure that the container specified in the SAS token matches the dataflow destination setting in the configuration.

Create a Kubernetes secret with the SAS token. Don't include the question mark ? that might be at the beginning of the token.

kubectl create secret generic <SAS_SECRET_NAME> \
--from-literal=accessToken='sv=2022-11-02&ss=b&srt=c&sp=rwdlax&se=2023-07-22T05:47:40Z&st=2023-07-21T21:47:40Z&spr=https&sig=<signature>' \
-n azure-iot-operations

You can also use the IoT Operations portal to create and manage the secret. To learn more, see Create and manage secrets in Azure IoT Operations Preview.

Finally, create the DataflowEndpoint resource with the secret reference.

dataLakeStorageSettings:
  authentication:
    method: AccessToken
    accessTokenSettings:
      secretRef: <SAS_SECRET_NAME>

User-assigned managed identity

To use a user-assigned managed identity, specify the UserAssignedManagedIdentity authentication method and provide the clientId and tenantId of the managed identity.

dataLakeStorageSettings:
  authentication:
    method: UserAssignedManagedIdentity
    userAssignedManagedIdentitySettings:
      clientId: <ID>
      tenantId: <ID>

Advanced settings

You can set advanced settings for the Azure Data Lake Storage Gen2 endpoint, such as the batching latency and message count.

Use the batching settings to configure the maximum number of messages and the maximum latency before the messages are sent to the destination. This setting is useful when you want to optimize for network bandwidth and reduce the number of requests to the destination.

Field Description Required
latencySeconds The maximum number of seconds to wait before sending the messages to the destination. The default value is 60 seconds. No
maxMessages The maximum number of messages to send to the destination. The default value is 100000 messages. No

For example, to configure the maximum number of messages to 1000 and the maximum latency to 100 seconds, use the following settings:

dataLakeStorageSettings:
  batching:
    latencySeconds: 100
    maxMessages: 1000