The Bit Bucket

Day by day, we’re getting further away from needing to use 32-bit DLLs. One place where I keep coming across them, is when doing integration work. The problem is that there are many 32-bit drivers for older systems, and no-one is going to ever bother to write 64-bit drivers for them. So, if you have to use those drivers, you need to run the 32-bit versions. And SSIS has options that allow you to run 32-bit drivers.

2026-06-29

While Power BI remains a fantastic visualization tool for business reporting, and we’ll talk more about it later, Fabric Real-Time Intelligence also includes its own built-in real-time dashboards — designed specifically for operational monitoring and live analytics.

These dashboards are native to the Real-Time Intelligence workspace. They connect directly to KQL Databases or Eventhouses, and they display query results that automatically refresh — often in near real time, with sub-second latency.

2026-06-28

Two of the parameters in SQL Server connections are the Application Name and the Host Name. You can see these in SQL Server if you execute the following command:

SELECT HOST_NAME() AS HostName, APP_NAME() AS ApplicationName;

On my system, that returns:

I’ve always been a fan of having applications identify themselves in their connection strings. It makes tasks like tracing much easier. The tools supplied with SQL Server do a reasonable job of that as you can see above. But many other tools don’t do such a good job.

2026-06-27

Trend analysis is one of the most powerful applications of KQL — it lets you move beyond simple metrics and start understanding how your data evolves over time.

Most trend analysis begins with the make-series operator. This operator constructs a time-series dataset by grouping values into evenly spaced time intervals. You might, for instance, calculate the average response time or error count every minute, hour, or day.

Here’s a simple example:

2026-06-26

One of the common challenges that arises in many applications is around how to optimize the SQL Server performance of dynamic search queries. For example, we’ve all seen applications with screens like this:

Often, there will be many, many more fields to be searched.

Perceived wisdom

What I often hear from developers is that there is no way to optimize the query because you don’t know in advance which options the user will choose. While this is partly true, it assumes that human behaviour is much more random than it really is.

2026-06-25

Anomaly detection is a key use case for real-time analytics — it’s about automatically identifying when something unusual or unexpected happens in your data.

In most systems, normal behavior forms a fairly predictable pattern. For example, transactions follow a steady daily rhythm, CPU usage fluctuates within a typical range, and sensor readings stay within expected bounds.

Anomalies are the data points that break from those patterns — a sudden spike in temperature, a large payment outside normal limits, or a sudden burst of failed login attempts.

2026-06-24

Another Fabric Down Under podcast is out the door.

Andrew is the Founder & CEO at Blue Badge Insights, and writes about Data & Analytics. He does independent analyst work with Gigaom.

Andrew is a fellow long-term MVP and member of the Microsoft Regional Director program and he’s co-chair of SQL Server Live! and Artificial Intelligence Live!

In the podcast, we discussed semantic models and ontologies, from the beginning to the current state of Fabric IQ. Andrew also shared interesting details from the competitive (data-related) industry landscape.

2026-06-23

KQL provides a comprehensive library of functions that make it incredibly flexible for analytics. You’ll find functions across several categories — from simple text operations to advanced time-series analysis.

First, there’s a wide range of built-in functions. These include string functions like substring(), tolower(), and replace(); mathematical functions such as round(), sqrt(), and abs(); and datetime functions like now(), ago(), and datetime_diff(). These make it easy to clean, transform, and standardize your data right inside your query.

2026-06-22

For general index maintenance, our friend Old Hallengren has an awesome solution for most people: https://ola.hallengren.com/sql-server-index-and-statistics-maintenance.html

We started to have customers using clustered columnstore indexes from SQL Server 2014, and they needed to be treated differently. At the time, I checked Ola’s latest scripts to see what happens with columnstore indexes.

It appeared that the code ignored nonclustered columnstore indexes (ie: index type of 6), which made sense as we would potentially need to rebuild them whenever the data changes, and at the time, the table was read-only. So that made lots of sense. Fortunately, that limitation is now gone.

2026-06-21

When you start working with real telemetry data, you’ll quickly notice that not everything fits neatly into tables and columns. Much of it is semi-structured — think JSON payloads, arrays, and nested fields.

KQL provides a set of advanced operators that make handling this kind of data easy and powerful.

The parse and parse_json operators let you extract values directly from text or JSON fields. For example, if your logs contain a JSON blob, you can pull out fields like deviceId or errorCode as first-class columns you can filter and aggregate on.

2026-06-20