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 Service Master Key (SMK) is one of the most misunderstood security components in SQL Server.
It is frequently described as:
Those statements are not entirely wrong - but they are incomplete, and in some cases actively misleading.
It’s important to understand what the Service Master Key actually does, when it matters, when it doesn’t, and why many operational decisions around it are based on myth rather than mechanics.
2026-01-30
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.
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
One of SQL Server’s greatest strengths is its ability to execute queries in parallel. When parallelism is available, the engine can divide work across multiple CPU cores and dramatically reduce query duration for large or complex workloads.
Yet in real systems, it’s surprisingly common to find queries that could run in parallel but don’t. The reason is often not hardware, configuration, or cost threshold settings — but query anti-patterns that quietly force the optimizer into a serial plan.
2026-01-26
One of the more frustrating classes of SQL Server application failures is also one of the most avoidable: collation conflicts involving tempdb.
I see these issues all the time. Developers create applications that require specific collations at the server level, because they don’t handle temporary tables (and by extension) tempdb well.
All this can be avoided, and you can easily build applications that will work with temporary tables, without worrying about the server (and tempdb) collation.
2026-01-26
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
This is the thirteenth tale in a series of stories about data. I hope you enjoy the series.
Today’s tale relates to a SQL Server system that had wonderful hardware, yet was unbelievably slow. Let’s discuss what happened.
I had a call from the Microsoft office in Brisbane, Queensland, asking if I had time to look at a problem at a customer site. Both they, and the customer, had thrown considerable resources at trying to resolve a performance problem, but frustratingly, it persisted.
2026-01-24
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
When government funding is tight, it gets harder and harder to get grants to perform fundamental research. The government always wants to see outcomes, and this means that the grants committees need to show outcomes. In turn, this often leads to research funding bodies doing one of three things:
Now while at first glance, that might sound a reasonable way to proceed, it’s not.
2026-01-22
