Integrate the Remote Monitoring solution with Azure Data Lake Store

You may have advanced analytics requirements beyond what is offered in the Remote Monitoring solution. Azure Data Lake Store is ideal for this application because it can store data from massive and diverse datasets as well as integrate with Azure Data Lake Analytics to provide on-demand analytics.

In this how-to, you will use an Azure Stream Analytics job to stream data from the IoT hub in your Remote Monitoring solution to an Azure Data Lake Store.


To complete this how-to, you will need the following:

Create a consumer group

Create a dedicated consumer group in the IoT hub of your Remote Monitoring solution. This will be used by the Stream Analytics job for streaming data to your Data Lake Store.


Consumer groups are used by applications to pull data from Azure IoT Hub. You should create a new consumer group for every five output consumers. You can create up to 32 consumer groups.

  1. Sign in to the Azure portal.

  2. In the Azure portal, click the Cloud Shell button.

    Portal Launch Icon

  3. Execute this command to create a new consumer group:

az iot hub consumer-group create --hub-name contoso-rm30263 --name streamanalyticsjob --resource-group contoso-rm


Use the resource group and IoT hub names from your Remote Monitoring solution.

Create Stream Analytics Job

Create an Azure Stream Analytics job to stream the data from your IoT hub to your Azure Data Lake store.

  1. Click Create a resource, select Internet of Things from the Marketplace, and click Stream Analytics job.

    New Stream Analytics Job

  2. Enter a job name and select the appropriate Subscription and Resource group.

  3. Select a Location in the near or in the same region as your Data Lake Store. Here we are using East US.

  4. Ensure to leave the Hosting environment as the default Cloud.

  5. Click Create.

    Create Stream Analytics Job

Configure the Stream Analytics job

  1. Go to the Stream Analytics job in your Remote Monitoring solution resource group.

  2. On the Overview page, click Inputs.

    Overview Page

  3. Click Add stream input and select IoT Hub from the drop-down.

    Add Input

  4. On the New input tab, enter an Input alias of IoTHub.

  5. From the Consumer group drop-down, select the consumer group you created earlier. Here we are using streamanalyticsjob.

    Select Input

  6. Click Save.

  7. On the Overview page, click Outputs.

    Add Data Lake Store

  8. Click Add and select Data Lake Store from the drop-down.

    Add Output

  9. On the New output tab, enter an Output alias of DataLakeStore.

  10. Select the Data Lake Store account you created in previous steps and provide folder structure to stream data to the store.

  11. In the Date format field, enter /streaming/{date}/{time}. Leave the default Date format of YYYY/MM/DD and Time format of HH.

    Provide Folder Structure

  12. Click Authorize.

    You will have to authorize with Data Lake Store to give the Stream analytics job write access to the file system.

    Authorize Stream Analytics to Data Lake Store

    You will see a popup and once the popup closes Authorize button will be greyed out after authorization is complete.


    If you see an error in the popup window, open a new browser window in Incognito Mode and try again.

  13. Click Save.

Edit the Stream Analytics query

Azure Stream Analytics uses a SQL-like query language to specify an input source that streams data, transform that data as desired, and output to a variety of storage or processing destinations.

  1. On the Overview tab, click Edit query.

    Edit Query

  2. In the Query editor, replace the [YourOutputAlias] and [YourInputAlias] placeholders with the values you defined previously.

        *, System.Timestamp as time

    Stream Analytics Query

  3. Click Save.

  4. Click Yes to accept the changes.

Start the Stream Analytics job

  1. On the Overview tab, click Start.

    Start Stream Analytics Job

  2. On the Start job tab, click Custom.

  3. Set custom time to go back a few hours to pick up data from when your device has started streaming.

  4. Click Start.

    Pick Custom Date

    Wait until job goes into running state, if you see errors it could be from your query, make sure to verify that the syntax is correct.

    Job running

    The streaming job will begin to read data from your IoT Hub and store the data in your Data Lake Store. It may take a few minutes for the data to begin to appear in your Data Lake Store.

Explore the streaming data

  1. Go to your Data Lake Store.

  2. On the Overview tab, click Data explorer.

  3. In the Data explorer, drill down to the /streaming folder. You will see folders created with YYYY/MM/DD format.

    Screenshot that shows the path to the /streaming/YYYY/MM/DD/HH folder.

    You will see json files with one file per hour.

    Explore Streaming Data

Next Steps

Azure Data Lake Analytics can be used to perform big data analysis on your Data Lake Store data sets. Learn more on the Data Lake Analytics Documentation.