The art of simplicity

Over the course of my career, I've been part of multiple initiatives to start internal communities at work. Some of them became something genuinely special — people showing up, contributing, looking forward to the next gathering. Others quietly died after a few months, victims of low attendance and dwindling energy. For a long time I couldn't figure out what separated the successes from the failures. Was it the topic? The timing? The right people involved? I kept searching for the formula. Then I listened to an episode of the ReThinking podcast last week where Adam Grant sat down with Dan Coyle — author of The Culture Code and his new book Flourish — and one thing Coyle said stopped me in my tracks. Community, he pointed out, literally means shared gifts . And shared gifts aren't something you passively receive. They're something you participate in . We've been thinking about it the wrong way Maybe you’ve tried to build an internal community before. You ...

Being new to Microsoft Fabric I noticed that you have multiple options when writing notebooks using Python: run your code with PySpark (backed by a Spark cluster) or with Python (running natively on the notebook's compute). Both options look almost identical on the surface — you're still writing Python syntax either way — but under the hood they behave very differently, and picking the wrong one can cost you time, money, and unnecessary complexity. In this post I try to identify the key differences and give you some heuristics for deciding which engine to reach for. Python vs PySpark: what's actually different? When you select PySpark in a Fabric notebook, your code runs on a distributed Apache Spark cluster. Fabric spins up a cluster, distributes your data across multiple worker nodes, and executes transformations in parallel. The core abstraction is the DataFrame (or RDD), and operations are lazy — nothing actually runs until you trigger an action like .show() ...

Ever find yourself mapping multiple string values to the same result? Being a C# developer for a long time, I sometimes forget that the C# has evolved so I still dare to chain case labels or reach for a dictionary. Of course with pattern matching this is no longer necessary. With pattern matching, you can express things inline, declaratively, and with zero repetition. A small example I was working on a small script that should invoke different actions depending on the environment. As our developers were using different variations for the same environment e.g.  "tst" alongside "test" , "prd" alongside "prod" .  We asked to streamline this a long time ago, but as these things happen, we still see variations in the wild. This brought me to the following code that is a perfect example for pattern matching: The or keyword here is a logical pattern combinator , not a boolean operator. It matches if either of the specified pattern...

Yesterday I showed how we could query local parquet and delta files using pandas and deltalake. Although these libraries work, once you start loading big parquet files you see your system stall while your memory usage spikes. A colleague suggested to give DuckDB a try. I never heard about it, but let’s discover it together. What is DuckDB? DuckDB is an in-process analytical database — think SQLite, but built for OLAP workloads instead of transactional ones. It runs entirely inside your Python process (no server to spin up, no connection string to manage) and is optimized for the kinds of queries data engineers run every day: large scans, aggregations, joins, and window functions over columnar data. A few things that make it stand out: It reads files directly. You don't import data into DuckDB before querying it. You point it at a Parquet file, a folder of Parquet files, or a Delta table, and it queries them in place. No ETL step, no intermediate copy. It's c...

Last week I shared how you could use the  OneLake File Explorer to sync your Lakehouse tables to your local machine. It's a convenient way to get your Parquet and Delta Lake files off the cloud and onto disk — but what do you actually do with them once they're there? In this post, I’ll walk you through how to interact with your locally synced OneLake files using Python. We'll cover four practical approaches, with real code you can drop straight into a notebook. Where are your files? When OneLake File Explorer syncs your files, they land in a path that looks something like this: C:\Users\<you>\OneLake - <workspace name>\<lakehouse name>.Lakehouse\Tables\<table name> Keep that path in mind— you'll be passing it into every example below. Delta Lake tables are stored as folders containing multiple Parquet files plus a _delta_log/ directory, so make sure you're pointing at the table's root folder, not an individual file. Readin...

If you've spent any time working with Microsoft Fabric, you know that navigating to the web portal every time you need to inspect, upload, or tweak a file gets old fast. OneLake File Explorer is Microsoft's answer to that friction — a lightweight Windows application that mounts your entire Fabric data estate directly in Windows File Explorer, the same way OneDrive handles your documents. One..what? OneLake is the unified data lake underpinning every Microsoft Fabric tenant. Unlike traditional architectures where teams maintain separate data lakes per domain or business unit, every Fabric tenant gets exactly one OneLake — one place where Lakehouses, Warehouses, KQL databases, and other Fabric items store their data. There's no need to copy data between engines; Spark, SQL, and Power BI all read from the same underlying storage. The organizational hierarchy is straightforward: Tenant → Workspaces → Items (Lakehouses, Warehouses, etc.) → Files/Tables . This maps neat...

I mostly use the built-in functionality to set a tag on a specific commit after a successful release. However, in this case I was contacted by a colleague who was using the Git Tag task . Unfortunately, he couldn’t get the task working. A look at the build log made it obvious what the problem was: Starting: GitTag ============================================================================== Task         : Git Tag Description  : A simple task that tags a commit Version      : 7.0.0 Author       : ATP P&I IT Help         : tags the current commit with a specified tag. ### Prerequisites * Repository must be VSTS Git. * Allow scripts to access Oauth must be **Enabled** * Project Collection Build Service must have **Contribute** & **Create Tag** set to **Allow** or **Inherit Allow** for that particular repository =======...

If you've ever found yourself repeating the same Azure setup ritual — adding the Application Insights SDK, wiring up telemetry, configuring sampling rules — you already know the pain. It's not hard, but it's tedious. Every new service needs the same scaffolding. Every new team member has to learn the same conventions. That's exactly what I solved with a custom skill. Now, when I need to instrument a service, I just tell Copilot to configure Application Insights, and it does everything exactly the way our team expects. No extra prompting, no re-explaining our conventions. It just works. This post explains what Skills are, how they work inside VS Code, and how to build one for your own team — using my Application Insights skill as a hands-on example. What is an agent skill? An agent skill is a folder of instructions, scripts, and reference files that teaches your AI agent how to handle a specific task. Think of it as institutional knowledge made executable. Instea...

I was working on a refactoring using VS Code the other day when I noticed something I hadn't seen before: a tiny bar chart quietly living in the status bar, tracking my AI vs. manual typing usage over the last 24 hours. It's called AI Statistics, and it shipped in VS Code 1.103. To enable it, open settings and search for "AI stats" — flip the checkbox, and a small gauge appears in the bottom-right of your status bar. Hover over it and you get a breakdown: how much of your recent code came from AI completions versus your own keystrokes. On the surface it sounds like a novelty. But I found myself actually pausing when I saw the numbers. It reframed something I hadn't really thought about consciously: not whether AI coding tools are good or bad, but just how much I'm actually leaning on them day to day. That visibility is weirdly valuable. It's the kind of data point that makes you more intentional — maybe you lean in harder on AI for boilerplate an...

Last year Valentine's Day I built a Romantic Movie Generator — an app that turned action movies into sweeping romantic dramas - using AI. Die Hard became a tender love story about a man who just wanted to spend Christmas with his wife. It was fun, it was silly, and it required a surprising amount of hand-holding to get the AI to behave. At that time a colleague took my idea and crafted his own version; Loveflix . This year, my partner made it abundantly clear that another "action movie as romance" project wasn't going to cut it for February 14th. Fair enough. So I did what any reasonable developer does under domestic pressure: I flipped the concept entirely. Built on top of the version from my colleague I created: ActionFlix: turn any rom-com into a high-octane action thriller. Because Love Actually is basically a heist movie if you squint hard enough. Same concept, inverted. Sweet home setup, chaos onscreen. Points successfully gained. But here's ...

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...