The Bit Bucket

More SQL and AI love !

We’ve been working on this for a while. PostgreSQL is a great vector database that can support your AI-based applications. And we’ve just completed a course that can show you how to use it. It’s low-cost ($195 USD) and offers our lifetime access.

Check it out and enrol now here: Using AI Features in PostgreSQL

Course Summary

Do you want to start working with AI-related features in PostgreSQL? Or are you going to be asked about using databases this way? Either way, you need to be prepared!

2026-10-03

As with any preview feature, there are some practical considerations.

The first is that this is still evolving. Not every scenario or data type is supported yet. Expect some limits on dataset size, refresh frequency, and concurrent analyses.

You must enable the Python plugin in the Eventhouse. This is what provides the model runtime, and enabling it isn’t instant. If you attempt analysis before it’s ready, the system will return an error.

2026-10-02

I’ve done a lot of Microsoft exams over the years, mostly SQL Server ones but plenty of others too. And one thing that I really don’t like is when the questions are:

• Purely academic (ie: would never happen)
• Memory-based (ie: who cares what a particular limit is for a particular SKU today?)
• Clearly not written by someone who actually uses the product

Today, I want to mention an item in the last category.

2026-10-01

Let’s look at where this feature fits in practice.

The first major category is IoT telemetry - monitoring physical equipment. Think of a manufacturing plant where each machine sends readings for vibration, current draw, and temperature. Sudden spikes or drifts in these metrics may indicate wear, imbalance, or failure. The anomaly detector can continuously flag these irregularities before they cause downtime.

Another use case is application or system performance. If you’re monitoring throughput, error rates, or latency across multiple services, anomalies may indicate deployment issues, scaling problems, or outages. Because detection runs continuously and in near real time, operations teams can be notified within seconds.

2026-09-30

One question that I’m asked all the time when consulting is whether reducing the size of database tables will make queries run faster or not.

The underlying question is typically about whether the client should implement some sort of archiving strategy, to reduce the amount of data in a table by moving older data off into another table.

My answer is that it might help, but if it does, you probably have another issue that would be a better one to solve instead.

2026-09-29

The AI Anomaly Detector in Microsoft Fabric Real-Time Intelligence uses a combination of time-series analysis and machine learning models. You don’t need to choose the model manually-Fabric evaluates the data and automatically selects the most appropriate approach.

Behind the scenes, it can use classical models like seasonal decomposition or ARIMA for steady, predictable series, or neural network–based models for more complex and irregular data. It also supports hybrid methods that adapt to both short-term trends and cope with long-term seasonality.

2026-09-28

I’m involved in a lot of performance tuning work for SQL Server based applications. These are mostly your typical OLTP accounting, financial, or record keeping applications. One thing that constantly surprises me is that many developers and DBAs really aren’t sure about where their tuning efforts need to be focused.

In particular, what really surprises me is how much focus most people have on write/update behavior and how little on read behavior. I think there’s a perception problem.

2026-09-27

The workflow is straightforward once you know the moving parts. You begin in your Eventhouse - that’s where data from your event streams is stored.

First, ensure that the Python plugin is enabled. This plugin provides the AI runtime used to train and run anomaly models. It can take up to an hour to enable for the first time, so plan that in advance.

2026-09-26

Perhaps I’m getting old and philosophical but I recall the days when the IT industry prided itself on accuracy. We worried when things didn’t work properly and kept working on them till they did work.

If a customer’s system stopped in a weird way, they actually wanted to know why. They didn’t just want to reboot it and hope for the best.

When did that change?

In a previous blog post , I’ve wrote about the evil Oops something went wrong syndrome that seems to have infected many Sofware as a Service applications.

2026-09-25

This new capability in Microsoft Fabric Real-Time Intelligence brings true AI-driven anomaly detection into the streaming environment. Traditionally, anomaly detection required a fair amount of data-science work - building models, testing parameters, and maintaining code. Fabric now handles much of that automatically.

The service examines incoming time-series or streaming data to look for unusual patterns - things that differ significantly from what’s been observed before. It supports both univariate detection, where you track a single metric such as temperature, latency, or transaction volume, and multivariate detection, which handles correlated signals - for example, temperature, pressure, and vibration from the same machine.

2026-09-24