Learn how to configure an event-driven data pipeline that automatically gets triggered when a new file lands in OneLake or Azure Blob Storage, to ingest and transform the new file.

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!

When working with raw JSON data in Eventhouse, one common challenge is comparing two records to identify what changed—especially when the structure isn’t fixed. Fields may appear, disappear, or shift in type, making traditional column-based comparisons brittle or outright impossible. This post will walk you through how to compare two JSON arrays and find what changed without needing to define the schema first. 

If you've ever seen a demo of Microsoft Fabric's Data Activator, it probably involved simulated data and pre-recorded spikes in dashboards. While the technology is undeniably powerful, it's often hard to connect those demos to something tangible.

In this post, I’ll walk you through how I used Data Activator, Azure Event Hub, and a Raspberry Pi connected to my electric panel to monitor the real-time energy usage of my house. This isn't just a mock-up — I can trigger real alerts by turning on real appliances in my kitchen.

Following quentincarton's deep dive into integrating ServiceNow with Microsoft Fabric Real Time Intelligence, this follow-up explores how to truly harness that data once it's flowing. From real-time dashboards and text analytics to automated alerts and Power BI reporting, discover how to turn your ServiceNow incidents into actionable insights—live, as they happen. Learn how Eventhouses and Notebooks in Fabric can help you extract key phrases, visualize trends, and even trigger downstream processes in real time.

🔍Ready to see your ticketing data in a whole new light? Dive into the possibilities!

When monitoring real-time data, you want updates as soon as new data comes in, but without unnecessary refreshes. This is where page refreshes based on change detection in Power BI become useful.

For example, if new data arrives every five minutes, refreshing a report ten times per minute is inefficient. Power BI’s change detection refresh option updates the page only when a specific field changes!

Migrating to Microsoft Fabric Real-Time Intelligence (RTI) from Azure Data Explorer (ADX) offers several benefits for organizations looking to enhance their data analytics capabilities. This article outlines how to efficiently migrate from ADX to Fabric RTI. Dive into the future of data analytics with Fabric RTI and see how it can transform your business operations.

Join a Microsoft Fabric Reddit Ask me Anything on March 5th at 10 AM PT with the Real-Time Intelligence team! 

Discover how to enhance your Azure/Fabric Data Factory pipelines with real-time telemetry using Microsoft Fabric RTI. Learn to seamlessly send diagnostic data to Eventhouses, enabling you to create dynamic dashboards and alerts for better performance monitoring and error troubleshooting. This guide walks you through setting up a flexible, schema-agnostic table and integrating it with your pipelines for instant insights. Transform your data analytics with ease and efficiency—explore the full potential of Fabric Real Time Intelligence today!

So, you’ve started exploring real-time intelligence in Fabric and maybe even followed the Microsoft tutorial to implement a real-time solution. Now, you’re looking to analyze and visualize that data, and that’s where KQL (Kusto Query Language) comes in.

When I started, I struggled a bit, so I thought it would be a good idea to provide some basic queries (and explain what they do) for people who want to get started but are unfamiliar with querying data.