In this quickstart, you populate a real-time dashboard to capture insights from the OPC UA data that you sent to Event Hubs in the previous quickstart. Using Microsoft Fabric Real-Time Intelligence, you bring your data from Event Hubs into Microsoft Fabric, and map it into a KQL database that can be a source for real-time dashboards. Then, you build a dashboard to display that data in visual tiles that capture insights and show the values over time.
These operations are the last steps in the sample end-to-end quickstart experience, which goes from deploying Azure IoT Operations at the edge through getting insights from that device data in the cloud.
Prerequisites
Before you begin this quickstart, complete the previous Azure IoT Operations quickstarts.
You also need to have the following Fabric resources:
A Microsoft Fabric subscription. In your subscription, you need access to a workspace with Contributor or above permissions.
A Fabric tenant that allows the creation of real-time dashboards. Your tenant administrator can enable this setting. For more information, see Enable tenant settings in the admin portal.
What problem will we solve?
When your OPC UA data arrives in the cloud, there's a lot of information available to analyze. You might want to organize that data and create reports containing graphs and visualizations to derive insights from the data. The steps in this quickstart illustrate how to connect that data to Real-Time Intelligence and create a real-time dashboard.
Ingest data into Real-Time Intelligence
In this section, you set up a Microsoft Fabric eventstream to connect your event hub to a KQL database in Real-Time Intelligence. This process includes setting up a data mapping to transform the payload data from its JSON format to columns in KQL.
Create an eventstream
In this section, you create an eventstream to bring your data from Event Hubs into Microsoft Fabric Real-Time Intelligence, and eventually into a KQL database.
Add your event hub as a data source for a new eventstream. For detailed instructions, see Get events from Azure Event Hubs into Real-time hub. As you add the data source, keep the following notes in mind:
Edit the Eventstream name to something friendly in the Stream details pane.
For Azure Event Hub Key, use the default selection (RootManageSharedAccessKey).
For Connection, create a new connection with Shared Access Key authentication. The connection credential details fill automatically.
Make sure local authentication is enabled on your Event Hubs namespace. You can set this authentication from the namespace's Overview page in the Azure portal.
For Consumer group, use the default selection ($Default).
For Data format, use the default selection (Json).
After connecting the eventstream, use the Open Eventstream button to see it in the authoring canvas. The stream from your Azure event hub is visible as an eventstream source.
Verify data flow
Follow these steps to check your work so far, and make sure data is flowing into the eventstream.
Start your cluster where you deployed Azure IoT Operations in earlier quickstarts. The OPC PLC simulator you deployed with your Azure IoT Operations instance should begin running and sending data. You can confirm this step by verifying that your event hub is receiving messages in the Azure portal.
Wait a few minutes for data to propagate. Then, in the eventstream live view, select the eventstream source and refresh the Data preview. You should see JSON data from the simulator begin to appear in the table.
Съвет
If data isn't arriving in your eventstream, check your event hub activity to isolate which section of the flow to debug.
Prepare KQL resources
In this section, you create a KQL database in your Microsoft Fabric workspace to use as a destination for your data.
First, create a Real-Time Intelligence Eventhouse (for detailed instructions, see Create an Eventhouse). When the Eventhouse is created, it automatically contains a default KQL database of the same name.
Next, create a new table in the default database in your Eventhouse (for detailed instructions, see Create an empty table in your KQL database). Name it OPCUA and manually enter the following schema.
Column name
Data type
AssetId
string
Spike
bool
Temperature
decimal
FillWeight
decimal
EnergyUse
decimal
Timestamp
datetime
After you create the OPCUA table, select it and use the Query with code button to open any sample query in a new query window for the table.
Clear the sample query, and run the following KQL query that creates a data mapping for your table. The data mapping is called opcua_mapping.
Next, add your eventstream as a data source for your KQL table. For detailed instructions, see Get data from Eventstream. As you add the data source, keep the following notes in mind:
Use the OPCUA table as the destination table and your eventstream as the source.
On the Inspect step, open the Advanced options. Under Mapping, select Existing mapping and choose opcua_mapping.
After you complete this setup, data begins to flow through your eventstream and is processed into your KQL table.
Wait a few minutes for data to propagate. Then, select the OPCUA table (you might need to refresh the view) to see a preview of the data from the eventstream appearing in the table.
You can also use the Query with code button to open a query window for the OPCUA table and run queries to explore the data.
Create a real-time dashboard
In this section, you create a new real-time dashboard to visualize your quickstart data, and import a set of tiles from a sample dashboard template. The dashboard allows filtering by asset ID and timestamp, and displays visual summaries of temperature, spike frequency, and other data.
Бележка
You can only create real-time dashboards if your tenant admin enabled the creation of real-time dashboards in your Fabric tenant. For more information, see Enable tenant settings in the admin portal.
Create dashboard
Navigate to your workspace and create a new real-time dashboard from the Real-Time Intelligence capabilities. For detailed instructions, see Create a new dashboard.
Then, follow these steps to upload the dashboard template and connect it to your data.
In your real-time dashboard, switch to the Manage tab and select Replace with file.
Select the template file that you downloaded to your machine.
The template file populates the dashboard with multiple tiles, although the tiles can't get data because you haven't connected a data source yet.
From the Manage tab, select Data sources. This action opens the Data sources pane with a sample source for your AIO data. Select the pencil icon to edit the AIOdata data source.
Choose your database (it's under OneLake data hub). When you're finished connecting your data source, select Apply and close the Data sources pane.
The visuals populate with the data from your KQL database.
On the Home tab, select Save to save your dashboard.
Explore dashboard
You now have a dashboard that displays different types of visuals for the asset data in these quickstarts. The visuals included with the template are:
Parameters for your dashboard that enable filtering of all visuals by timestamp (included by default) and asset ID.
A line chart tile showing temperature and its spikes over time.
A stat tile showing a real-time spike indicator for temperature. The tile displays the most recent temperature value, and if that value is a spike, conditional formatting displays it as a warning.
A stat tile showing max temperature.
A stat tile showing the number of spikes in the selected time frame.
A line chart tile showing temperature versus fill weight over time.
A line chart tile showing temperature versus energy use over time.
From here, you can experiment with the filters and adding other tile types to see how a dashboard can enable you to do more with your data.
This step completes the quickstart flow for using Azure IoT Operations to manage device data from deployment through analysis in the cloud.
Clean up resources
Now that you're finished with the quickstart experience, this section contains instructions to delete your sample resources.
If you want to remove the Azure IoT Operations deployment but keep your cluster, use the az iot ops delete command:
Azure CLI
az iot ops delete --cluster$CLUSTER_NAME--resource-group$RESOURCE_GROUP
If you want to delete all the resources you created for this quickstart, delete the Kubernetes cluster where you deployed Azure IoT Operations and then remove the Azure resource group that contained the cluster.
If you used Codespaces for these quickstarts, delete your Codespace from GitHub.
Бележка
The resource group contains the Event Hubs namespace you created in this quickstart.
You can also delete your Microsoft Fabric workspace and all the resources within it associated with this quickstart, including the eventstream, Eventhouse, and real-time dashboard. Additionally, you might want to delete the dashboard template file that you downloaded to your computer.
Analysis of real-time data streams is a critical capability for any modern data analytics solution. You can use the Real-Time Intelligence capabilities of Microsoft Fabric to ingest, query, and process streams of data.
As a Fabric analytics engineer associate, you should have subject matter expertise in designing, creating, and deploying enterprise-scale data analytics solutions.
