Microsoft-Fabric

In the past, if you wanted a real-time Power BI dashboard, you had to use streaming datasets — data pushed directly into Power BI from an external source.

While that approach worked, it came with significant limitations: the data couldn’t easily be joined with historical information, retention was limited, and managing the push model was complex.

That model is now deprecated — streaming datasets shouldn’t be used for new solutions.

2026-07-10

Thresholds are great for simple conditions — but they fall short when what’s normal changes throughout the day, week, or season. That’s where anomaly detection comes in.

Instead of relying on fixed limits, anomaly detection uses statistical and machine learning techniques to model what’s normal for a given signal, then flag data points that deviate from that pattern.

For example, network traffic might be high during business hours but low overnight — a single static threshold would either miss issues or trigger constant false alarms. Anomaly detection adjusts dynamically, recognizing these natural variations in the data.

2026-07-08

In real-time analytics, thresholds are the simplest way to detect when something goes outside of normal operating conditions.

A threshold is just a fixed value or condition applied to a metric. For example, you might set a rule to flag when CPU usage stays above 90% for more than five minutes, or when transaction latency exceeds two seconds.

In Fabric Real-Time Intelligence, thresholds are typically implemented within KQL queries or dashboard visuals — for instance, using conditional formatting to highlight values above a certain limit, or defining an analytic rule to monitor a KPI in real time.

2026-07-06

In recent posts, I’ve focused on dashboards and visualizations — but real-time intelligence goes beyond that. The real power comes when your system doesn’t just display insights but can act on them automatically.

Dashboards are great for monitoring, but most people can’t sit and watch a dashboard all day. Instead, we can use alerts and automation to make the data work for us.

There are a few ways to do this in Microsoft Fabric and the broader Power Platform: Power BI Alerts can be set up on tiles or visuals. They monitor thresholds — like sales dropping below a target — and notify users when conditions are met.

2026-07-04

Warehouses in Microsoft Fabric are designed for structured, relational analytics — and they’re a perfect destination for streaming data that fits those patterns.

In many organizations, data warehouses have traditionally been refreshed through nightly or hourly batch loads. That meant Power BI dashboards and reports were always slightly behind — reflecting yesterday’s or last hour’s data.

With Fabric Eventstreams, we can now stream data directly into Warehouse tables, especially into fact tables that support BI models. This means data becomes queryable within seconds of arrival, rather than waiting for the next batch window.

2026-07-02

One of the most powerful features of Microsoft Fabric is the ability to stream data directly into a Lakehouse.

A Lakehouse is a unified storage system that brings together both structured and unstructured data. It can store raw files, like CSV or Parquet, as well as structured tables in Delta format. That means it’s equally comfortable handling real-time events and batch-loaded historical data.

When we connect Eventstreams to a Lakehouse, the incoming data is written directly into Delta tables. This format supports transactional consistency, so you can query the data immediately while new events continue to arrive.

2026-06-30

While Power BI remains a fantastic visualization tool for business reporting, and we’ll talk more about it later, Fabric Real-Time Intelligence also includes its own built-in real-time dashboards — designed specifically for operational monitoring and live analytics.

These dashboards are native to the Real-Time Intelligence workspace. They connect directly to KQL Databases or Eventhouses, and they display query results that automatically refresh — often in near real time, with sub-second latency.

2026-06-28

Trend analysis is one of the most powerful applications of KQL — it lets you move beyond simple metrics and start understanding how your data evolves over time.

Most trend analysis begins with the make-series operator. This operator constructs a time-series dataset by grouping values into evenly spaced time intervals. You might, for instance, calculate the average response time or error count every minute, hour, or day.

Here’s a simple example:

2026-06-26

Anomaly detection is a key use case for real-time analytics — it’s about automatically identifying when something unusual or unexpected happens in your data.

In most systems, normal behavior forms a fairly predictable pattern. For example, transactions follow a steady daily rhythm, CPU usage fluctuates within a typical range, and sensor readings stay within expected bounds.

Anomalies are the data points that break from those patterns — a sudden spike in temperature, a large payment outside normal limits, or a sudden burst of failed login attempts.

2026-06-24

Another Fabric Down Under podcast is out the door.

Andrew is the Founder & CEO at Blue Badge Insights, and writes about Data & Analytics. He does independent analyst work with Gigaom.

Andrew is a fellow long-term MVP and member of the Microsoft Regional Director program and he’s co-chair of SQL Server Live! and Artificial Intelligence Live!

In the podcast, we discussed semantic models and ontologies, from the beginning to the current state of Fabric IQ. Andrew also shared interesting details from the competitive (data-related) industry landscape.

2026-06-23