How to build a live interactive visual dashboard in Power BI with Azure Stream

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Azure Stream Analytics is a managed complex event processing interactive data engine. As a built-in output connector, it offers the facility of building live interactive intelligent BI charts and graphics using Microsoft’s cloud-based Business Intelligent tool called Power BI. In this tutorial we implement a data architecture pipeline by designing a visual dashboard using Microsoft Power BI and Stream Analytics.

Prerequisites of building an interactive visual live dashboard in Power BI with Stream Analytics:

  • Azure subscription
  • Power BI Office365 account (the account email ID should be the same for both
  • Azure and Power BI). It can be a work or school account

Integrating Power BI as an output job connector for Stream Analytics

To start with connecting the Power BI portal as an output of an existing Stream Analytics job, follow the given steps:

  1.  First, select Outputs in the Azure portal under JOB TOPOLOGY:

Stream Analytics job

  1.  After clicking on Outputs, click on +Add in the top left corner of the job window, as shown in the following screenshot:

Stream analytics job

  1.  After selecting +Add, you will be prompted to enter the New output connectors of the job. Provide details such as job Output name/alias; under Sink, choose Power BI from the drop-down menu.
  2.  On choosing Power BI as the streaming job output Sink, it will automatically prompt you to authorize the Power BI work/personal account with Azure. Additionally, you may create a new Power BI account by clicking on Signup. By authorizing, you are granting access to the Stream Analytics output permanently in the Power BI dashboard. You can also revoke the access by changing the password of the Power BI account or deleting the output/job.
  3.  Post the successful authorization of the Power BI account with Azure, there will be options to select Group Workspace, which is the Power BI tenant workspace where you may create the particular dataset to configure processed Stream Analytics events. Furthermore, you also need to define the Table Name as data output. Lastly, click on the Create button to integrate the Power BI data connector for real-time data visuals:

New output

 

Note: If you don’t have any custom workspace defined in the Power BI tenant, the default workspace is My Workspace. If you define a dataset and table name that already exists in another Stream Analytics job/output, it will be overwritten.

It is also recommended that you just define the dataset and table name under the specific tenant workspace in the job portal and not explicitly create them in Power BI tenants as Stream Analytics automatically creates them once the job starts and output events start to push into the Power BI dashboard.

 

  1. On starting the Streaming job with output events, the Power BI dataset would appear under the dataset tab following workspace. The  dataset can contain maximum 200,000 rows and supports real-time streaming events and historical BI report visuals as well:

PowerBI

  1. Further Power BI dashboard and reports can be implemented using the streaming dataset. Alternatively, you may also create tiles in custom dashboards by selecting CUSTOM STREAMING  DATA under REAL-TIME OATA, as shown in the following screenshot:

Add title

  1.  By selecting Next, the streaming dataset should be selected and then the visual type, respective fields, Axis, or legends, can be defined:

Custom streaming data file

  1. Thus, a complete interactive near real-time Power BI visual dashboard can be implemented with analyzed streamed data from Stream Analytics, as shown in the following screenshot, from the real-world Connected Car-Vehicle Telemetry analytics dashboard:

real-time visual dashboard

In this article we saw a step-by-step implementation of a real-time visual dashboard using Microsoft Power BI with processed data from Azure Stream Analytics as the output data connector.

This article is an excerpt from the book, Stream Analytics with Microsoft Azure, written by Anindita Basak, Krishna Venkataraman, Ryan Murphy, and Manpreet Singh. To learn more on designing and managing Stream Analytics jobs using reference data and utilizing petabyte-scale enterprise data store with Azure Data Lake Store, you may refer to this book.

Stream Analytics with Microsoft Azure

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