Fabric-Rti-101

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

When people hear the phrase real-time, their minds often jump straight to sub-second response. They imagine dashboards updating instantly or transactions being analyzed faster than the blink of an eye. But in reality, not every scenario demands that level of speed — and chasing sub-second performance everywhere can actually be counterproductive.

It’s helpful to think about real-time in classes of latency. At the extreme, you have sub-second responses — critical for areas like algorithmic trading, where even a few milliseconds can cost millions. Then there are scenarios where a response in a few seconds is perfectly adequate — for example, fraud detection. If you can flag a suspicious credit card transaction within two or three seconds, that’s usually enough to stop the transaction before it clears. And then there are other cases where even minutes are fine. Think about a customer experience dashboard in a call center — if the screen refreshes every minute or two, that still feels live enough to be useful.

2026-01-13

When we talk about real-time, it’s worth asking: why does it matter so much? The answer is that in many situations, timing is everything. Traditional business reports are incredibly valuable, but they often arrive hours or days after the fact.

By the time you’ve spotted the issue, the opportunity to act has already passed.

Take fraud detection as an example. If a customer’s credit card is being misused, you can’t afford to wait until the next daily reconciliation. By then, dozens of fraudulent purchases might have gone through. Real-time intelligence means the system can detect unusual patterns on the spot and stop the fraud before it escalates.

2026-01-11

This is the start of a new series of blog posts dedicated to Microsoft Fabric Real-Time Intelligence or RTI. I think RTI is an important aspect of Microsoft Fabric, yet it is currently underutilized. I hope to try to help change that.

So why, RTI ?

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.

2026-01-09