Microsoft-Fabric

Let’s talk about message brokers and event streams — these are the backbone technologies that make real-time systems work at scale.

At the simplest level, a message broker acts as a middleman between the systems producing events and the systems consuming them. Instead of producers and consumers being tightly coupled — where the producer has to know exactly where to send data and the consumer has to be available at the exact right moment — the broker sits in between and handles that communication.

2026-02-02

Most of the systems we’ve worked with historically are request-driven. In this model, a client asks for information and the server provides it. Think about browsing a website: you type in a URL, your browser requests the page, and the server responds with the content. That’s a pull model — the client decides when it wants data. It’s predictable, it’s synchronous, and it’s been the backbone of web applications for decades.

2026-01-31

The Real-Time hub is the single, tenant-wide entry point for working with real-time data in Microsoft Fabric. Every Fabric tenant has exactly one Real-Time hub, and it exists independently of individual workspaces.

Single logical place for streaming data

The Real-Time hub is the single, tenant-wide entry point for working with real-time data in Microsoft Fabric. Every Fabric tenant has exactly one Real-Time hub, and it exists independently of individual workspaces.

2026-01-29

We’ve talked about events, streams, ingestion, and processing — but all of that only really matters if we take the final step: taking action. Actions are what turn insight into outcomes.

It’s one thing to detect that something unusual has happened. Maybe you see a spike in failed logins, a sudden drop in sales, or an overheating sensor. But if you stop at simply noticing it, the value is lost. The real payoff comes when the system — or the people using it — can respond.

2026-01-27

Once events have been ingested, the next step is processing. This is where we take the raw firehose of events and turn it into something meaningful and usable.

NOTE: We’re not talking about storing the events in a database, and then querying them. We can do that, but here we’re talking about transforming the events in-flight. It’s similar to what we could do with Azure Stream Analytics. I like the description that says that instead of throwing a query at the data, you’re throwing the data at a query.

2026-01-25

Ingestion is the very first step in any real-time architecture: getting events from wherever they originate and bringing them into where they can be processed, analyzed, and acted on.

Events typically start their lives in all kinds of different systems. They might come from Kafka topics in an enterprise environment, AMQP brokers in messaging-based systems, or from Azure-native services like Event Hubs or IoT Hub, which are especially common in cloud and IoT scenarios. Ingestion is what connects those sources into Fabric. Without it, you don’t have anything to work with.

2026-01-23

Once we understand what an event is, the next concept is the stream. A stream is simply a continuous flow of events over time. Instead of looking at events one by one in isolation, a stream is what you get when you treat them as a live feed coming from a source.

For example, imagine an IoT scenario. Each sensor reading from a device is an event. But when you look at all those readings flowing in second by second, that becomes a stream of telemetry. In a financial system, every transaction is an event — but all transactions flowing in from your payment gateway form a transaction stream.

2026-01-21

In real-time intelligence, everything starts with the concept of an event. An event is the most fundamental unit of real-time data — it’s simply a record that something happened.

That something could be almost anything, depending on your business. In finance, an event might be a stock trade or a payment transaction. In a web application, it might be a customer clicking a button, logging in, or abandoning a shopping cart. In IoT, it could be a sensor reading like temperature, vibration, or GPS coordinates. Even a server log entry or an error message can be considered an event.

2026-01-19

Microsoft Fabric Real-Time Intelligence is a complete toolkit — an end-to-end set of capabilities that allow you to take streaming data, make sense of it, and act on it.

It starts with event ingestion. Fabric can connect to a wide range of streaming sources: Kafka, Azure Event Hubs, IoT Hub, and many others. That means whether your data is coming from IoT sensors, application logs, or business systems, you can bring it all into Fabric without a lot of custom wiring. Fabric RTI doesn’t force you to pick one source — it’s designed to be open and flexible.

2026-01-17

Batch processing has been the backbone of data analytics for decades. The idea is simple: you collect data over a period of time, maybe hours or a whole day, and then process it in one big chunk. This is how traditional ETL pipelines and overnight data warehouse loads work. It’s efficient when immediacy doesn’t matter — for example, producing a daily sales report each morning.

But the limitation is obvious: if you need to react quickly, batch just doesn’t cut it. By definition, you’re waiting for the batch window to complete before you see the results. If fraud is happening right now, or if a customer is struggling with your app this very minute, a batch report tomorrow morning is far too late.

2026-01-15