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suparnababu8

Building a Real-Time Weather Dashboard in Microsoft Fabric: Step-by-Step Guide

Step 1: Create the Workspace & Eventhouse

Start by creating a new workspace named RealTime-Weather-WS.

  1. Click New Item.
    suparnababu8_0-1765297310059.png

     

  2. Search for Eventhouse.
    suparnababu8_0-1765297396931.png

     



  3. Select Eventhouse and create a new instance named Bengaluru-weather-EH.
    suparnababu8_0-1765297539995.png

Once created, Fabric automatically provisions an associated KQL Database.

suparnababu8_1-1765297565073.png

 

Step 2: Create an Eventstream

Navigate back to the workspace and:

  1. Click New Item
    suparnababu8_2-1765297592807.png
  2. Search for Eventstream
    suparnababu8_3-1765297630567.png
  3. Create one named bengaluru-weather-stream
    suparnababu8_4-1765297653823.png

This will open the Eventstream canvas where we’ll add our real-time weather sources.

 

Step 3: Add Weather Feed Sources

On the Eventstream canvas:

  1. Click Add source --> Connect data source
    suparnababu8_0-1765297736868.png
  2. Select New
    suparnababu8_1-1765297763457.png
  3. Choose Public Feeds --> Real-time weather
    suparnababu8_2-1765297786877.png

Search for Kengeri – Bengaluru, rename the source to kengeri, and add it.

suparnababu8_3-1765297806182.png

The live Kengeri weather feed now appears on the canvas.

suparnababu8_4-1765297840186.png

suparnababu8_5-1765297853303.png

Step 4: Add Additional Bengaluru Localities

Repeat the same steps to add more suburban Bengaluru locations.

Following this process, add all 8 Bengaluru localities you want to monitor.

suparnababu8_0-1765297908198.png

Once done, you’ll see all streaming sources producing live data simultaneously.

suparnababu8_1-1765297939830.png

Step 5: Add a Destination (Eventhouse)

Now we send the streaming data to Eventhouse.

  1. Click Add Destination --> Eventhouse
    suparnababu8_2-1765297967094.png
  2. Choose the previously created Bengaluru-weather-EH. Map the eventstream output to the Eventhouse table then click Save
    suparnababu8_0-1765298336075.png

Next:

  1. Insert a processing node
    suparnababu8_1-1765298360340.png
  2. Select Manage Fields
    suparnababu8_2-1765298386534.png
  3. Add important fields such as Latitude, DateTime, and others required for reporting as follows
    suparnababu8_0-1765298451921.pngsuparnababu8_1-1765298471159.pngsuparnababu8_2-1765298490647.png

     

After configuring the fields, save your changes and publish the Eventstream.

suparnababu8_0-1765298532702.png

At this point, your weather data is flowing live into Eventhouse.

suparnababu8_1-1765298557005.png

 

Step 6: Build the Real-Time Dashboard

Navigate to the Eventhouse database and open the table (bengalurudb).Click on Real-Time Dashboard.

suparnababu8_2-1765298579848.png

In edit mode:

  1. Select a tile and click Edit
    suparnababu8_0-1765298626021.png
  2. Add your KQL query
    suparnababu8_1-1765298647360.png
  3. Configure the visual mapping and apply changes
    suparnababu8_2-1765298667508.png

     

For example, the temperature map tile uses a KQL query that extracts temperature data and plots it across locations.

Add additional tiles—for example:

  • Location-wise temperature distribution
  • Wind speed
  • Humidity
  • Real-time metrics per suburb

After configuring all visuals, click Save.

suparnababu8_3-1765298684371.png

Your Real-Time Weather Dashboard is now fully operational.

suparnababu8_4-1765298708454.png

 

Final View

Once all components are published, your workspace will include:

  • Eventhouse
  • Eventstream
  • KQL Database
  • Real-Time Dashboard
  • Data mappings and transformations
    suparnababu8_5-1765298729856.png

This completes your real-time weather monitoring solution in Microsoft Fabric.

 

Conclusion

That’s how you can build an end-to-end Real-Time Weather Data Dashboard in Microsoft Fabric using public weather feeds.
I hope you found this blog helpful. If you have any questions or want to explore more scenarios, feel free to reach out.

Happy learning!

Acknowledgements
I would like to express my sincere gratitude to @SuryaTejaJosyul   and @rajendraongole1  for their continuous guidance and support throughout this Real-Time Intelligence (RTI) implementation. Their insights and encouragement played a key role in helping me complete this solution successfully.

 

-- Inturi Suparna Babu

[LinkedIn]

Comments

That's very useful for real-time data sets

Excellent Blog for Real Time Data Analysis in Fabric

Here is end-to-end tutorialhttps://youtu.be/UikXmBF3sR8?si=y-dbjSkF08uKMUZg

 

Well summarized, thanks a lot