It was great to catch up with Jerry Nixon today and to have him on a SQL Down Under podcast.
I recently had Jess Pomfret on a show, discussing Data API Builder. And the logical extension of that is to talk about SQL MCP Server.
Jerry is a Principal Product Manager for Data and AI at Microsoft.
He says he is focused on the developer persona to minimize onboarding friction and maximize value.
2026-05-26
Also in June this year, the AI Edition of the Data Science Summit is coming. And this time I’m presenting two sessions.
The first session is Using AI Features in SQL Server, and I’ll be discussing the AI features that have been added to SQL Server in 2025.
Use the code DSSAI26SP20 to get 20% off Standard or Exec passes.
See you at DSS!
Find more details about the event here: https://ml.dssconf.pl/
2026-05-25
One of the things I always tell people that I love about consulting/mentoring work is that you see things that you just can’t make up. They provide interesting material for training classes.
Recently, I came across something that I wasn’t expecting. I was migrating data from DB2 and in my scripts, I had made the presumption that a table would have at least one column. Turns out that in DB2 you can have a table with no columns.
2026-05-24
On June 15-16, @MS Tech Summit 2026 is on again! I enjoy this event every year, and have participated for many years.
My session this time is How AI Features Improve Search in SQL Server 2025, and I’ll be discussing how AI-based capabilities in SQL Server have changed the game for search.
Use the code MSTS26SP20 to get 20% off Standard or Exec passes.
See you at MSTS!
Find more details about the event here: https://ml.dssconf.pl/en/
2026-05-23
In Azure SQL Database, a database server is a logical concept that’s used to keep details of connected databases. It does have a master database but it’s not like the on-premises equivalent.
It’s pretty much a container for logins. And it won’t let you create objects in it. I wish it would as there is occasionally code that I’d like to store at the server level. An example is code that I use to control scaling of databases from within T-SQL.
2026-05-22
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
