The Bit Bucket

Thresholds are great for simple conditions — but they fall short when what’s normal changes throughout the day, week, or season. That’s where anomaly detection comes in.

Instead of relying on fixed limits, anomaly detection uses statistical and machine learning techniques to model what’s normal for a given signal, then flag data points that deviate from that pattern.

For example, network traffic might be high during business hours but low overnight — a single static threshold would either miss issues or trigger constant false alarms. Anomaly detection adjusts dynamically, recognizing these natural variations in the data.

2026-07-08

Memory is a precious resource on most systems today. SQL Server loves memory, so it’s important to avoid wasting it.

One common cause of wasted memory is what we call plan cache pollution. The plan cache is the region where SQL Server stores compiled query plans (and query plan stubs).

There are some nice new DMVs that give you details of what’s in that cache but nothing still compares to one of the older compatibility views sys.syscacheobjects. You can use it to see what’s in your plan cache:

2026-07-07

In real-time analytics, thresholds are the simplest way to detect when something goes outside of normal operating conditions.

A threshold is just a fixed value or condition applied to a metric. For example, you might set a rule to flag when CPU usage stays above 90% for more than five minutes, or when transaction latency exceeds two seconds.

In Fabric Real-Time Intelligence, thresholds are typically implemented within KQL queries or dashboard visuals — for instance, using conditional formatting to highlight values above a certain limit, or defining an analytic rule to monitor a KPI in real time.

2026-07-06

I spend most of my life in a variety of customer sites. Most of these nowadays tend to be large financial organizations. When I talk to developers in those organizations, and ask them about how they interact with their DBAs, I can’t tell you how often I hear that they try to avoid ever making schema changes, etc. as it takes too long and the process is painful.

When I talk to the DBAs at the same organizations, and I ask them why they resist making schema changes, I hear this:

2026-07-05

In recent posts, I’ve focused on dashboards and visualizations — but real-time intelligence goes beyond that. The real power comes when your system doesn’t just display insights but can act on them automatically.

Dashboards are great for monitoring, but most people can’t sit and watch a dashboard all day. Instead, we can use alerts and automation to make the data work for us.

There are a few ways to do this in Microsoft Fabric and the broader Power Platform: Power BI Alerts can be set up on tiles or visuals. They monitor thresholds — like sales dropping below a target — and notify users when conditions are met.

2026-07-04

I recently received a review copy of Agentic Coding with Claude Code by Eden Marco from my friends at PackT.

Author

Eden Marco is an LLM specialist at Google Cloud and a LangChain Ambassador with years of experience in software engineering and cloud architecture. Over many years, he has created practical courses based on his real-world experience.

Content

This is a timely and practical guide for developers who are ready to move beyond casual AI-assisted coding and start treating coding agents as serious development tools. Eden’s main argument is that effective agentic coding is not just about writing better prompts. It is about engineering context: deciding what the agent should know, what it should ignore, what tools it should use, and how its work should be structured so that results are repeatable, controlled, and maintainable.

2026-07-03

Warehouses in Microsoft Fabric are designed for structured, relational analytics — and they’re a perfect destination for streaming data that fits those patterns.

In many organizations, data warehouses have traditionally been refreshed through nightly or hourly batch loads. That meant Power BI dashboards and reports were always slightly behind — reflecting yesterday’s or last hour’s data.

With Fabric Eventstreams, we can now stream data directly into Warehouse tables, especially into fact tables that support BI models. This means data becomes queryable within seconds of arrival, rather than waiting for the next batch window.

2026-07-02

Note: This is part of a series of posts where I’m improving some of my older blog posts.

Recently, I wrote about one of the issues with the Invoke-SqlCmd commandlet where it sets the ApplicationName when you use the parameter that should set the host.

Fellow MVP Ben Miller sent me a copy of the Invoke-SqlCmd3 that they were using. It was much better.

I then realized that there were many other options missing from these commandlets (such as options for Database Mirroring and Availablity Groups) and so I set about improving it.

2026-07-01

One of the most powerful features of Microsoft Fabric is the ability to stream data directly into a Lakehouse.

A Lakehouse is a unified storage system that brings together both structured and unstructured data. It can store raw files, like CSV or Parquet, as well as structured tables in Delta format. That means it’s equally comfortable handling real-time events and batch-loaded historical data.

When we connect Eventstreams to a Lakehouse, the incoming data is written directly into Delta tables. This format supports transactional consistency, so you can query the data immediately while new events continue to arrive.

2026-06-30

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