Stream Analytics and Power BI: A real-time analytics dashboard for streaming data
Azure Stream Analytics enables you to take advantage of one of the leading business intelligence tools, Microsoft Power BI. In this article, you learn how create business intelligence tools by using Power BI as an output for your Azure Stream Analytics jobs. You also learn how to create and use a real-time dashboard.
This article continues from the Stream Analytics real-time fraud detection tutorial. It builds on the workflow created in that tutorial and adds a Power BI output so that you can visualize fraudulent phone calls that are detected by a Streaming Analytics job.
You can watch a video that illustrates this scenario.
Before you start, make sure you have the following:
- An Azure account.
- An account for Power BI Pro. You can use a work account or a school account.
- A completed version of the real-time fraud detection tutorial. The tutorial includes an app that generates fictitious telephone-call metadata. In the tutorial, you create an event hub and send the streaming phone call data to the event hub. You write a query that detects fraudulent calls (calls from the same number at the same time in different locations).
Add Power BI output
In the real-time fraud detection tutorial, the output is sent to Azure Blob storage. In this section, you add an output that sends information to Power BI.
In the Azure portal, open the Streaming Analytics job that you created earlier. If you used the suggested name, the job is named
On the left menu, select Outputs under Job topology. Then, select + Add and choose Power BI from the dropdown menu.
Select + Add > Power BI. Then fill the form with the following details and select Authorize to use your own user identity to connect to Power BI (the token is valid for 90 days).
For production jobs, we recommend to connect to use Managed Identity to authenticate your Azure Stream Analytics job to Power BI.
If Power BI has a dataset and table that have the same names as the ones that you specify in the Stream Analytics job, the existing ones are overwritten. We recommend that you do not explicitly create this dataset and table in your Power BI account. They are automatically created when you start your Stream Analytics job and the job starts pumping output into Power BI. If your job query doesn't return any results, the dataset and table are not created.
When you select Authorize, a pop-up window opens and you are asked to provide credentials to authenticate to your Power BI account. Once the authorization is successful, Save the settings.
The dataset is created with the following settings:
- defaultRetentionPolicy: BasicFIFO - Data is FIFO, with a maximum of 200,000 rows.
- defaultMode: pushStreaming - The dataset supports both streaming tiles and traditional report-based visuals (also known as push).
Currently, you can't create datasets with other flags.
For more information about Power BI datasets, see the Power BI REST API reference.
Write the query
Close the Outputs blade and return to the job blade.
Click the Query box.
Enter the following query. This query is similar to the self-join query you created in the fraud-detection tutorial. The difference is that this query sends results to the new output you created (
If you did not name the input
CallStreamin the fraud-detection tutorial, substitute your name for
CallStreamin the FROM and JOIN clauses in the query.
/* Our criteria for fraud: Calls made from the same caller to two phone switches in different locations (for example, Australia and Europe) within five seconds */ SELECT System.Timestamp AS WindowEnd, COUNT(*) AS FraudulentCalls INTO "CallStream-PowerBI" FROM "CallStream" CS1 TIMESTAMP BY CallRecTime JOIN "CallStream" CS2 TIMESTAMP BY CallRecTime /* Where the caller is the same, as indicated by IMSI (International Mobile Subscriber Identity) */ ON CS1.CallingIMSI = CS2.CallingIMSI /* ...and date between CS1 and CS2 is between one and five seconds */ AND DATEDIFF(ss, CS1, CS2) BETWEEN 1 AND 5 /* Where the switch location is different */ WHERE CS1.SwitchNum != CS2.SwitchNum GROUP BY TumblingWindow(Duration(second, 1))
Test the query
This section is optional, but recommended.
If the TelcoStreaming app is not currently running, start it by following these steps:
Open Command Prompt.
Go to the folder where the telcogenerator.exe and modified telcodatagen.exe.config files are.
Run the following command:
telcodatagen.exe 1000 .2 2
On the Query page for your Stream Analytics job, click the dots next to the
CallStreaminput and then select Sample data from input.
Specify that you want three minutes' worth of data and click OK. Wait until you're notified that the data has been sampled.
Click Test and review the results.
Run the job
Make sure the TelcoStreaming app is running.
Navigate to the Overview page for your Stream Analytics job and select Start.
Your Streaming Analytics job starts looking for fraudulent calls in the incoming stream. The job also creates the dataset and table in Power BI and starts sending data about the fraudulent calls to them.
Create the dashboard in Power BI
Go to Powerbi.com and sign in with your work or school account. If the Stream Analytics job query outputs results, you see that your dataset is already created:
In your workspace, click + Create.
Create a new dashboard and name it
At the top of the window, click Add tile, select CUSTOM STREAMING DATA, and then click Next.
Under YOUR DATSETS, select your dataset and then click Next.
Under Visualization Type, select Card, and then in the Fields list, select fraudulentcalls.
Fill in tile details like a title and subtitle.
Now you have a fraud counter!
Follow the steps again to add a tile (starting with step 4). This time, do the following:
When you get to Visualization Type, select Line chart.
Add an axis and select windowend.
Add a value and select fraudulentcalls.
For Time window to display, select the last 10 minutes.
Click Next, add a title and subtitle, and click Apply.
The Power BI dashboard now gives you two views of data about fraudulent calls as detected in the streaming data.
Learn about limitations and best practices
Currently, Power BI can be called roughly once per second. Streaming visuals support packets of 15 KB. Beyond that, streaming visuals fail (but push continues to work). Because of these limitations, Power BI lends itself most naturally to cases where Azure Stream Analytics does a significant data load reduction. We recommend using a Tumbling window or Hopping window to ensure that data push is at most one push per second, and that your query lands within the throughput requirements.
You can use the following equation to compute the value to give your window in seconds:
- You have 1,000 devices sending data at one-second intervals.
- You are using the Power BI Pro SKU that supports 1,000,000 rows per hour.
- You want to publish the amount of average data per device to Power BI.
As a result, the equation becomes:
Given this configuration, you can change the original query to the following:
SELECT MAX(hmdt) AS hmdt, MAX(temp) AS temp, System.TimeStamp AS time, dspl INTO "CallStream-PowerBI" FROM Input TIMESTAMP BY time GROUP BY TUMBLINGWINDOW(ss,4), dspl
If the password has changed since your job was created or last authenticated, you need to reauthenticate your Power BI account. If Azure Multi-Factor Authentication is configured on your Azure Active Directory (Azure AD) tenant, you also need to renew Power BI authorization every two weeks. If you don't renew, you could see symptoms such as a lack of job output or an
Authenticate user error in the operation logs.
Similarly, if a job starts after the token has expired, an error occurs and the job fails. To resolve this issue, stop the job that's running and go to your Power BI output. To avoid data loss, select the Renew authorization link, and then restart your job from the Last Stopped Time.
After the authorization has been refreshed with Power BI, a green alert appears in the authorization area to reflect that the issue has been resolved.