The art of simplicity

Wow! We covered a lot in this series. Part 1 - Overview & Architecture Part 2 – Data collection with Azure Arc Part 3 – Data persistence in Log Analytics Part 4 -  Data visualization with Azure Workbooks Time for a wrap up and some troubleshooting Let's trace the data flow from start to finish to make sure everything connects: The Azure Monitor Agent runs on each Arc-enabled on-prem VM. The Data Collection Rule tells the agent what health data to gather — application pools, Windows services, and scheduled tasks. The agent collects that data on a regular interval and ships it to Azure. The DCR routes the incoming data to our custom table ( OnPremHealthStatus_CL ) in the Log Analytics Workspace. The Workbook queries that table and renders the dashboard. If any link in that chain breaks, data stops flowing. The troubleshooting section below covers the most common failure points. Troubleshooting checklist No data appearing in the workbook: ...

If you've ever found yourself repeatedly correcting GitHub Copilot with the same preferences or re-explaining your team's coding standards in every chat session, the January 2026 release of VS Code brings a possible solution: Copilot Memory . What Is Copilot Memory? Copilot Memory is a new feature that allows GitHub Copilot to remember important context across your coding sessions. Think of it as giving your AI assistant a notebook where it can jot down your preferences, team conventions, and project-specific guidelines—and actually refer back to them later. Released as a preview feature in VS Code version 1.109 (January 2026), Copilot Memory changes how you interact with AI-powered coding assistance by making your conversations with Copilot persistent and personalized. How it works The magic of Copilot Memory happens through a new memory tool that's integrated directly into VS Code's chat interface. Here's how it works: Intelligent detection Copilot a...

In part 1 I explained that we want to setup an application health dashboard to gain insights on the availability and health of the on-premise parts of our applications. Specifically, we want to monitor our application pools, scheduled tasks and windows services. I introduced the overall architecture and explained the building blocks. Part 2 was all about the data collection part using Azure Arc Data Collection rules. I continued in Part 3 with our custom table in Log Analytics to persist our data. And today it is time for Part 4 were I share how visualize all this info using Azure Workbooks. What we're visualizing The workbook is the user-facing piece. Our goal is a dashboard that lets an operator quickly answer three questions: What's running? What's stopped or failed? Which machines need attention? A good health dashboard has two modes: the "glance" mode where an operator can immediately see if anything is wrong, and the "investigate" mod...

If you're using GitHub Copilot in Visual Studio Code, there's a powerful new feature that can save you time and make your AI-powered development workflow more efficient: the /init command. This slash command provides a quick way to set up custom instructions for your workspace or adapt your existing instructions to the specific project context, helping you establish consistent coding practices and AI responses across your projects. What is the /init command? The /init command is a chat slash command in VSCode that helps you quickly prime your workspace with custom instructions for GitHub Copilot. When you type /init in the chat input box, it automatically generates a .github/copilot-instructions.md file tailored to your workspace. Think of it as a quick-start wizard for setting up AI guidelines that will influence how Copilot generates code and handles development tasks throughout your project. How to use the /init command? Using the /init command is straightforw...

In part 1 I explained that we want to setup an application health dashboard to gain insights on the availability and health of the on-premise parts of our applications. Specifically we want to monitor our application pools, scheduled tasks and windows services. I introduced the overall architecture and explained the building blocks. Part 2 was all about the data collection part using Azure Arc Data Collection rules. Today I’ll focus on how we used a custom table in Log Analytics to persist our data. Why a custom table The built-in Windows event logs in Log Analytics (the Event table) contain a lot of data, but the format isn't optimized for health-status queries. Parsing event log XML to extract service states or scheduled task results on every query adds latency and complexity. When you query the Event table for service state changes, you're filtering through thousands of rows, parsing semi-structured XML from the EventData column, and then correlating multiple ev...

In part 1 I explained that we want to setup an application health dashboard to gain insights on the availability and health of the on-premise parts of our applications. Specifically we want to monitor our application pools, scheduled tasks and windows services. I introduced the overall architecture and explained the building blocks. Today we'll dive in the first one of these blocks; the data collection part using Azure Arc Data Collection rules. Understanding Data Collection rules A Data Collection Rule (DCR) is a declarative configuration object in Azure that defines the full lifecycle of telemetry: what to collect, how to transform it, and where to send it. It's the connective tissue between the Azure Monitor Agent running on your VMs and the Log Analytics Workspace where the data lands. DCRs replaced the older model where agents were configured locally via XML files. The new model is centralized — you define the DCR in Azure, associate it with your VMs, and the agent...