The Bit Bucket

While KQL joins are focused on live or streaming data, SQL joins become important once that data has been persisted — typically into a warehouse, lakehouse, or relational store inside Fabric. At that stage, you’re working with stored tables, not continuous streams, and SQL provides the familiar, well-established way to combine them.

For example, suppose your eventstream has been writing processed data into a table every few seconds — say, a record of transactions, or aggregated IoT readings. Once that data is stored, you can use SQL to join it with enterprise datasets: things like master data, financial records, or customer relationship tables that live in the warehouse.

2026-05-21

When creating tools that script out SQL Server objects, the normal behavior is to quote object names, in case the names wouldn’t otherwise be valid. The problem with that, is that many scripts then become an amazing mess of square brackets that makes the scripts far less readable. And often, there’s no need for the quoting in the first place.

If you avoid things like spaces in object names, you can mostly get away without quoting i.e. Sales.Customers is just fine and doesn’t need to be [Sales].[Customers]. That’s just far less readable IMHO.

2026-05-20

One of the really practical features of KQL databases in Microsoft Fabric is that they don’t lock you into a single query language. While KQL, or Kusto Query Language, is the native language of these databases, you can also query them using standard SQL syntax, including T-SQL style statements that most data professionals are already comfortable with.

This is an important bridge for teams coming from a relational database background. If you’ve spent years working with SQL Server, Azure SQL, or even PostgreSQL, the idea of learning an entirely new language just to access telemetry or event data can feel like a hurdle.

2026-05-19

We work with many customers that are moving some of their applications to cloud-based systems, and mostly on Azure. One message that I often hear about using Azure SQL Database (ASD) is that all you need to do is point your application’s connection string to the cloud and all will be good. While there are occasional cases where that is true, that generally isn’t going to give you a great outcome. And it’s generally very misleading. To really get a great outcome, you generally will need to check out how your application has been designed.

2026-05-18

When we talk about joining in KQL, we’re usually dealing with a real-time or near-real-time stream of data that needs extra context. The stream itself might contain the facts about what just happened — an event, a transaction, a sensor reading — but to understand that event properly, we often need information that isn’t included in the stream.

That additional context usually comes from a reference or historical table. For example, if you’re processing IoT sensor data, you might want to know what type of device sent the reading, what its normal operating range is, or which location it’s installed in. The stream doesn’t carry that metadata on every message because that would make the data unnecessarily large. Instead, you can use a join to combine the live stream with a reference dataset that contains those attributes.

2026-05-17

I have days where I can’t decide if I’m frustrated or sad about how I see SQL Server being used by applications, or if I’m happy that this keeps us in ongoing work.

Recently, I was looking at a system that was having performance issues. There were three key applications on the system. Each came from a different vendor and when I looked at how each one of them interacts with the server, it was just really frustrating.

2026-05-16

A KQL queryset in Microsoft Fabric is essentially a container or workspace for related KQL queries. Instead of having dozens of individual queries saved in isolation, a queryset lets you group them in one logical place so they can be organized, reused, and managed more effectively.

Think of it as a structured collection — you might have a queryset dedicated to application performance monitoring, another for network telemetry, and another for error diagnostics. Each queryset can hold multiple KQL queries, each focused on a specific metric or analysis, but all connected by a common purpose.

2026-05-15

Why learn ?

I was listening to one of Scott Hanselman’s awesome podcasts with Miguel de Icaza and during the show they discussed things that you really should invest in. One list that came up was a good bed, a good chair, and learning to type. Most of us spend 1/3 of the day in a bed and 1/3 of the day in a chair, so they seem like no-brainers. Typing is an interesting addition to that list.

2026-05-14

On June 15-16, I’ll be participating in @MS Tech Summit 2026! Each year, @MSTechSummit is a great event.

Join me for my session How AI Features Improve Search in SQL Server 2025 , where I’ll be discussing how the addition of AI-based capabilities in SQL Server has changed the game for search.

Use the code MSTS26SP20 to get 20% off Standard or Exec passes.

See you at DSS!

Find more details about the event here: https://ml.dssconf.pl/en/

2026-05-12

Show 4 with Guest Ron Dunn

2026-05-12