Global Aircraft✈️ Live Tracking with Microsoft Fabric Real-Time Intelligence
In this blog, I’ll walk you through, build a real-time global flight tracking system. We will ingest live flight data from the public OpenSky Network API using a Python polling script, stream it through Microsoft Fabric Eventstream into an Eventhouse (KQL Database), transform the dense raw arrays using KQL update policies and visualize the results on a Real-Time Dashboard complete with maps, KPIs and analytical charts. Prerequisites: Valid Fabric Capacity / Trail License Knowledge on Python Knowledge on KQL Step1: Setup Workspace & Eventhouse (KQL Database) Created a workspace “FlightTracking-[WS]” Created an eventhouse “FLightTracking-EH” Create a raw ingestion table “RawFlightbatc” in KQL Databse Step 2: Setup a Fabric Eventstream Created a Eventstream “GlobalFlightStream” and select ‘Use custom endpoint’ Click ‘Add’ Click on ‘Publish’ Copy the ‘Event hub name’ and ‘connection string-primary key’ into notepad Step 3: Notebook Creation and Setup Python script Created a notebook. Make sure select Python Install azure eventhub package Let’s go back to eventstream and add destination by selecting ‘Eventhouse’ Configure all details and click on save Comeback to Notebook and insert the Python script which is having all the connection strings / passwords etc. Run the notebook Now notebook started running and sending the data to eventstream Data is loaded into eventstream and Click on Publish Now eventstream is ‘Live’ Data is loading into KQL Database Step 4: Regularizing Data with KQL & Update Policies Create a cleaned table “FlightStates” Create the parsing function and update policy Alter table with updated policy Step 5: Building Real-Time Dashboard Click on Realtime dashboard and give a name “FlightOperationsDashboard” Now click on edit Run the below code to get total active flights count Change the chart to Stat and rename, click on Apply KPI added and click on Add visual and take new Stat visual Insert the code and run to get India Origin Flights and Format it and click Apply In the same way, I built other KPIs Select Map Chart Run the below code and Fill all details and Click on Apply. Here we’re calculating Flights trend We can see chart added to Dashboard Select a Bar chart Run the below code, fill all details and click on apply to add Bar chart to Dashboard. Here we’re getting the top 10 countries by aircrafts Run the below code, fill all details and click on apply to add Column chart to Dashboard. Here we’re categorizing the baro-altitude which is critical for airport delay predection Run the below code, fill all details and click on apply to add Pie chart to Dashboard. It splits the aircraft parked versus those actively flying Together, these visuals transform raw flight telemetry into an operational monitoring experience. Key takeaways: Fabric Eventstream provides a streamlined way to ingest external real-time feeds. Eventhouse/KQL Database provides a real-time analytical environment for flight telemetry. KQL mv-expand simplifies the processing of nested flight-state arrays. Update Policies automate transformation from raw streaming data into structured analytical data. KQL geospatial functions enable location-based flight analysis. Real-Time Dashboards transform streaming telemetry into actionable operational insights. Conclusion: This project demonstrates how Microsoft Fabric Real-Time Intelligence can be used to build an end-to-end real-time aircraft tracking solution. we can transform continuously arriving aircraft telemetry into meaningful real-time insights. Do you want to replicate? Get Code file from my GitHub link You can find all the KQL queries, update policy functions, and the complete Python polling script in the official GitHub repository below: [Download from here] Acknowledgements I would like to express my sincere gratitude to @SuryaTejaJosyul , @minniwalia 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 successfull Happy learning! — Inturi Suparna Babu [LinkedIn]
Handling Multiple Schemas in Eventstream for Azure Event Hub and Azure IOT Hub data sources
One of the challenges with streaming data is that data comes in a variety of schemas, which can be dynamic and are not always predictable. As applications and data structures change, schema values can sometimes be wildly different across devices or event inputs. Recently a customer reached out with several questions on this common problem. With the capabilities in Fabric Real Time Intelligence we can flexibly ingest this data. Enough of the background let’s get to the problem!From Rain Drops to Playability: Scoring Real-Time Weather Insights for FIFA World Cup 2026⚽
In this blog, I will walk you through how we can support the FIFA World Cup 2026 by organizing teams and players by delivering real-time, actionable weather insights. By predicting critical weather patterns - such as high humidity, extreme heat, and sudden rainfall - teams can proactively adjust their game-day strategies, and organizers can ensure optimal player safety. To bring this vision to life, we will build a comprehensive, real-time weather monitoring dashboard covering all 16 FIFA World Cup stadiums, powered entirely by the real-time analytics capabilities of Microsoft FabricEventhouse vs Lakehouse - When to Use Which in Microsoft Fabric?
Microsoft Fabric offers multiple storage and analytical engines. This blog helps you make the right architectural choice early on and save them massive migration efforts later. In this blog, I’ll walk you through the differences between these two powerhouses, look at real-world challenges, map out concrete scenarios, and give you a definitive comparison framework so you can choose the right tool for the job.🚦From Signals to Insights: Bengaluru Smart City Traffic Updates with Fabric Real-Time Intelligence
In this blog, we will build a Bengaluru Smart City Traffic Updates Dashboard using Microsoft Fabric Real-Time Intelligence. We'll simulate live traffic signals from major Bengaluru junctions, stream events into Fabric, store them in a KQL database, and create an interactive real-time dashboard that provides actionable traffic insights. By streaming events into Fabric and leveraging powerful analytics, you’ll see how raw signals can be transformed into actionable insights for smarter urban mobility.Building Real-Time Indian Railways Dashboard in Microsoft Fabric – A step-by-step guide
In this blog, I’ll walk you through how to build a Real-Time Indian Railways Train Running Status Dashboard using Microsoft Fabric. We’ll stream live railway data from a public API into Eventstream, store it in Eventhouse, transform it using KQL, and finally visualize it in a real-time dashboard.Stop Polling, Start Reacting: Building Bulletproof Event-Driven Pipelines in Microsoft Fabric
I just stepped off the stage at SQLSaturday Atlanta and the energy around Microsoft Fabric is absolutely electric. Between the deep-dive sessions and the hallway conversations, one question kept coming up: “How do we make our data orchestration more efficient?” In the world of Synapse and Data Factory, we’ve been conditioned to “Poll.” We set a schedule to check a table every 15 minutes, 24/7. But in a Capacity-based world like Fabric, “Polling” is essentially a tax on your resources. Today, I’m sharing the framework I demoed in Atlanta – moving from expensive “Ghost Pipelines” to a lean, event-driven architecture that only works when there is a “doorbell” to answer.
Optimizing Real-Time Analytics in Microsoft Fabric RTI with Follower and Read-Optimized Databases
This article discusses about an architectural pattern to achieve better performance when trying to work with traditional relational database schema in Microsoft Fabric Real Time Intelligence (RTI). A common scenario is that you have Fabric as the enterprise data platform that ingests data from a live system running at scale. This article addresses two common requirements: You have streaming ingestion with variety of consumers for the data. You have use cases that demand fast response time even when the query is joining huge tables. The pattern discussed here uses a database shortcut in Fabric RTI, also known as a follower database, and a read-write database. With the database shortcut, each downstream system gets dedicated capacity and SLAs. With a read-write database, data can be denormalized and stored in a read-optimized format to improve real time query performance.
