KQL – Curated SQL

XML Processing in Microsoft Fabric Realtime Intelligence

Published 2026-03-16 by Kevin Feasel

I’ve been working for quite some time on a fun solution in Fabric Realtime Intelligence. We’re processing XML files into a structured table. As you’re probably aware, XML has its own… well, let’s be nice and call them challenges.

One thing I ran into was that an element contained several other elements. Usually, you’ll see them in an array, but in this case, it wasn’t. Since these elements within the main element contain the information we need for the table, I started thinking about how to extract this data.

Read on for an example of the type of Data Reitse was looking to process, as well as how the problem ended up being a lot easier to solve than first appearances would indicate.

Eventstream Not Sending Data to KQL Database after Resuming Fabric Capacity

Published 2026-03-11 by Kevin Feasel

Olivier Van Steenlandt troubleshoots an issue:

To continue the development of my mobile app, whose core ability is to scan barcodes of consumable articles and send them over for analytics, I’m resuming my capacity, starting to scan barcodes again, sending them to my Eventstream, and finally saving them in my KQL database.

After a couple of minutes, I wanted to validate all the scanned results in my KQL database and navigate to my scanned_barcode table.

Read on to see how Olivier diagnosed and corrected the problem.

Adaptive Time Series Visualization in Microsoft Fabric

Published 2026-02-13 by Kevin Feasel

Devang Shah and Slava Trofimov show off a design pattern:

This design pattern provides intuitive, interactive Fabric-native experiences for any user:

Intelligent time binning: Handle billions of data points by automatically grouping them into optimal intervals.

Time brushing: Zoom in any period with drag-and-select interactions.

Multi-metric comparison: View multiple time series side by side across different assets.

Flexible aggregation: Switch between average, min, max, and sum with a single selection.

Anomaly detection: KQL queries detect unusual patterns in your time series with no ML expertise required.

Statistical insights: View descriptive statistics and correlations.

Contextualization: Bring asset hierarchies, tag metadata, and definitions directly into the report for richer interpretation.

Read on to learn more about the pattern and how it works. There are a lot of moving parts to get right, but the end result looks impressive.

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A Primer on Fabric Real-Time Intelligence

Published 2026-01-09 by Kevin Feasel

Greg Low fills us in:

Let’s start with a simple idea. Real time intelligence (or RTI) is about shrinking the delay between when data is created and when you can act on it. In traditional systems, we’re often used to data being collected, stored, and only analyzed later, maybe overnight or even weekly. That’s fine for long term reporting, but it’s too slow for situations where immediate action matters.

Assume that I levy my standard complaint here about how “internet speed” is not real-time. But leaving that aside, Greg gives a few use cases for RTI, and I do think it’s a good part of the Microsoft Fabric platform.

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Eventhouse Endpoint for Fabric Data Warehouse

Published 2025-11-21 by Kevin Feasel

Tzvia Gitlin Troyna announces a new feature:

The new Eventhouse Endpoint for Fabric Data Warehouse extends this same architecture to structured data sources, allowing users to:

Query Fabric Data Warehouse tables in real-time using KQL.

Leverage schema mirroring for warehouse tables.

Unify analytics across Lakehouse and Fabric Data Warehouse without duplicating data.

Even if I don’t expect many data platform practitioners to use KQL and even though I’m morally opposed to the Fabric Data Warehouse (short version: Lakehouses and Warehouses in Fabric should be the same thing, not two separate things), I’d still consider this a step forward. It does provide a new integration point between two services that have been annoyingly isolated.

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Generating an Entity Diagram in a Fabric Eventhouse

Published 2025-11-04 by Kevin Feasel

Guy Reginiano announces a new tool:

As your KQL database grows, tables gather data from several Eventstreams, functions connect different tables, update policies move and transform data, and materialized views quietly keep aggregated data up to date – all working together behind the scenes

It’s powerful, but it can also be hard to see the full picture.

That’s exactly why we built the Entity Diagram – to give you a simple, visual way to explore how everything in your database connects.

Click through to see how it works.

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Learning Microsoft Fabric Real-Time Intelligence

Published 2025-10-24 by Kevin Feasel

Valerie Junk picks up a new skill:

If you are reading this article on my website, chances are you know me from my Power BI content, the videos, articles, tutorials, or downloads, or you came across it on LinkedIn. I want to be upfront: I am a front-end/business person. I create reports that lead to action and help businesses make smarter decisions while building a data-driven strategy.

When I started talking about Fabric Real-Time Intelligence, people were surprised. Some were curious. Others probably wondered what had happened. For me, real-time reports push you to approach design in a completely different way because users need to take action immediately. Decisions happen in the moment, and that changes everything about how you visualize and structure information, so that got me interested!

Read on to see how Valerie picked up KQL as a language, as well as some of the challenges involved. I will say, the Eventhouse is also the fastest mechanism Microsoft has to query large amounts of data in Microsoft Fabric—it beats out the lakehouse and warehouse pretty handily.

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Tips for Working with Real-Time Analytics in Microsoft Fabric

Published 2025-08-08 by Kevin Feasel

Reitse Eskens shares some tips:

When discussing options, possibilities, and solutions with customers, the Real-Time stack began to emerge. We received questions on ingestion that couldn’t be simply answered using batch processing. The best part is that we can start learning new technology!

The following blog will outline the best things I learned working with real-time analytics.

Click through for those items.

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Ingesting Logs into Microsoft Fabric Real-Time Intelligence via Logstash

Published 2025-07-15 by Kevin Feasel

Surya Teja Josyula and Ramachandran G. use one part of the ELK stack:

Logstash is an open-source data processing tool that enables the collection, transformation, and forwarding of data from a wide variety of sources. It acts as a data pipeline engine, helping organizations manage and streamline the flow of structured and unstructured data across systems.

Whether you’re managing infrastructure logs, application events, or telemetry data, this guide will walk you through setting up a seamless pipeline that bridges raw log data with real-time analytics in Fabric.

Click through for the process.

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Loading JSON into a Microsoft Fabric Eventhouse

Published 2025-05-23 by Kevin Feasel

Christopher Schmidt loads some data:

In the era of big data, efficiently parsing and analyzing JSON data is critical for gaining actionable insights. Leveraging Kusto, a powerful query engine developed by Microsoft, enhances the efficiency of handling JSON data, making it simpler and faster to derive meaningful patterns and trends. Perhaps more importantly, Kusto’s ability to easily parse simple or nested JSON makes it easier then ever to extract meaningful insights from this data. The purpose of this blog post is to walk through ways that JSON data can be loaded into Eventhouse in Microsoft Fabric, where you can then leverage Kusto’s powerful capabilities for this. I’ve tried this a few different ways, and the below approach is the fastest, most efficient low-code way to ingest the data into the Eventhouse. As JSON inherently supports different schemas in a single file, the expectation here is that we have a json file with varying schemas within a single file, and we would like to load this into our Eventhouse for efficient parsing with KQL.

Read on for the process.

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Category: KQL