Connect SaaS & Enterprise Systems with Azure Functions

In this Azure Friday episode, Scott Hanselman and Poorvi Narang explore why Microsoft built Azure Linux, an open-source Linux distribution optimized for Azure workloads. Poorvi demonstrates a cloud-native application running from a local WSL environment to Azure virtual machines and Azure Kubernetes Service, while highlighting Azure Linux's minimal footprint, familiar DNF package management, and security-by-default capabilities such as SELinux and firewall controls. They also discuss how to get started with the Azure Linux 4.0 public preview and its path to general availability. Chapters 00:00 - Introduction 00:32 - Why Microsoft built Azure Linux 03:09 - Local development with Azure Linux on WSL 05:19 - Deploying to an Azure virtual machine 12:47 - Deploying to AKS with Azure Container Linux 14:52 - Azure Linux 4.0 availability 16:09 - Resources and wrap-up Recommended resources Azure Linux documentation Azure Linux product page Azure Linux on GitHub Connect Scott Hanselman | Twitter/X: @SHanselman Azure Friday | Twitter/X: @AzureFriday Azure | Twitter/X: @Azure

Everyone is asking whether their workload belongs on a quantum computer, and most of the answers out there might surprise you. Werner Rall joins Scott Hanselman to demo the Quantum Advantage Evaluator, an app on Azure that gives an honest verdict - quantum, AI/ML, or classical HPC. Using Grover's search algorithm as the worked example: the algorithm runs perfectly, and the Azure Quantum Resource Estimator still says it would take 61,122 physical qubits to reliably search sixteen items. Werner then submits the same circuit as a real job to Azure Quantum simulator, and shows why the verdict is decided in code before the AI model is ever called. Chapters 00:00 - Introduction 00:33 - Quantum Advantage Intro 01:50 - Quantum Advantage Evaluator Architecture 03:18 - Scenario - Searching unsorted database - Grover 04:50 - Application Traceability 08:20 - Grover Search Quantum run 11:40 - Pareto Frontier from the Resource Estimator: what error correction costs 14:20 - Crux of the Quantum Advantage Tool 14:56 - Azure Quantum Workspace Demo 16:35 - Which Quantum Computers are available in Azure 18:09 - Running the Grover Search Job on Azure Quantum 20:19 - Wrap up Recommended resources Learn Docs Azure Quantum Quantum Grand Challenges on GitHub Connect Scott Hanselman | Twitter/X: @SHanselman Werner Rall | Twitter/X: @werneragent47 Azure Friday | Twitter/X: @AzureFriday Azure | Twitter/X: @Azure

This session showcases how Azure Functions enables highly scalable event-driven automation using managed connectors. It highlights how functions can seamlessly integrate with services like SharePoint, Teams, and more without manual polling or complex authentication handling. The demo emphasizes how triggers and actions simplify building real-world workflows with minimal code. Chapters 00:35 - Introduction 02:00 - Demo Briefing 03:27 - Connectors Portal Overview 03:55 - Functions Local Dev & Webhook Tunnel 05:42 - Triggers vs Actions 10:15 - Preview Features & Roadmap 12:16 - Resources Recommended resources Demonstrated Sample More Samples Learn Docs Azure Product page Connect Scott Hanselman | Twitter/X: @SHanselman Azure Friday | Twitter/X: @AzureFriday Azure | Twitter/X: @Azure

See how our open-source Logic Apps Migration Agent uses agentic refactoring to modernize BizTalk, TIBCO, MuleSoft, and other legacy integrations to Azure Logic Apps Standard. The talk demonstrates how the agent analyzes and transforms complex workloads to reduce migration effort and risk. Chapters 00:00 - Introduction 01:05 - BizTalk in the IT Landscape 01:30 - Why move from BizTalk to Logic Apps? 03:20 - Balancing LLMs with procedural steps to migrate 06:00 - Importance of Grounding for both technologies 06:34 - Demo Context: From BizTalk Server to Logic Apps Standard 07:32 - Discovery Phase 08:22 - Planning Phase 09:08 - Creation, Execution and Deployment Phases 14:34 - Learn more Recommended resources Migration Automation from Integration Platforms - Azure Logic Apps Workflow Integration and Automation - Azure Logic Apps Connect Scott Hanselman | Twitter/X: @SHanselman Harold Campos | YouTube: @hcamposu Azure Friday | Twitter/X: @AzureFriday Azure | Twitter/X: @Azure

In this Azure Friday episode, Scott Hanselman and Tulika Chaudharie show how to take an Aspire application from local development to Azure App Service for Linux. They demonstrate how to create and configure an Aspire project, deploy its frontend, backend, and dashboard as App Service resources, and use the hosted Aspire Dashboard to understand the running application. They also explore how the Aspire agent integration can help generate the required configuration and simplify the deployment experience. Chapters 00:00 - Introduction 01:45 - Creating an Aspire project 03:35 - Configuring AppHost for Azure App Service 09:15 - Exploring the deployed Azure resources 09:50 - Viewing resources in the Aspire Dashboard 11:05 - Running the sample application 12:00 - App Service configuration and service discovery 14:20 - Accessing the hosted Aspire Dashboard 16:00 - Aspire configuration and deployment guidance 17:45 - Aspire agent integration 19:35 - Wrap-up Recommended resources Quickstart - Deploy an Aspire app Azure App Service Connect Scott Hanselman | Twitter/X: @SHanselman Azure App Service | Twitter/X: @AzAppService Azure Friday | Twitter/X: @AzureFriday Azure | Twitter/X: @Azure

See how a single Azure Database for PostgreSQL Flexible Server scales read-heavy workloads using read replicas and virtual endpoints — the same pattern OpenAI relies on to run ChatGPT on Postgres. Scott and Paula show a live read workload saturating a primary, then offload it to a replica with a single hostname change, and finish with replicas across Europe staying within milliseconds of the primary. It's read scale-out without sharding or re-architecting your app. Chapters 00:30 - Introduction 01:55 - Why read replicas: read/write asymmetry 03:45 - Architecture: primary, replicas, and virtual endpoints 05:30 - Demo: the cluster and a read-only replica 10:28 - Baseline: saturating the primary 13:53 - The flip: offloading reads to a replica 16:48 - Wrap up Recommended resources Read replicas in Azure Database for PostgreSQL Azure Database for PostgreSQL Demo script Connect Scott Hanselman | Twitter/X: @SHanselman Paula Berenguel | LinkedIn: paulaberenguel Azure Friday | Twitter/X: @AzureFriday Azure | Twitter/X: @Azure

In this Azure Friday episode, Scott Hanselman and Sajee demonstrate the Azure Cosmos DB Agent Kit — a skill you install with one command that gives your coding agent 100+ Cosmos DB best-practice rules across data modeling, partitioning, query optimization, and SDK usage and much more. Using a multi-agent fitness coaching app as an example, they show how the kit caught a missing partition key filter that was leaking member data across tenants, recommended hierarchical partitioning for multi-tenant scale, and fixed a fan-out query—all before the code shipped to production. Chapters 00:00 - Introduction 00:33 - Meet Sajee & overview of the Cosmos DB Agent Kit 00:50 - The problem: partition key & query mistakes that cost money in production 02:32 - How the Agent Kit works: one install, 100+ rules across 12 categories 04:52 - Demo setup: fitness coaching multi-agent app with Cosmos DB 06:26 - Showing the data: missing partition key filter exposes other members' data 08:23 - Agent Kit findings: SQL injection, singleton pattern, fan-out queries 10:46 - Indexing best practices & query optimization recommendations 12:34 - Applying the fix: correct results and single-partition RU cost 13:13 - Wrap up & how to get started Recommended resources Azure Cosmos DB Agent Kit Agent Kit Repository Connect Scott Hanselman | Twitter/X: @SHanselman Sajeetharan | Twitter/X: @sajeetharan Azure Friday | Twitter/X: @AzureFriday Azure | Twitter/X: @Azure

Azure HorizonDB is a new Postgres service on Azure built for scale, availability, and performance across any workload. In this episode of Azure Friday, we look at how HorizonDB delivers predictable performance with built-in zone resilience. We also explore new in-database AI features like AI Model Management and AI Pipelines, and walk through the developer experience in VS Code to build, query, and manage efficiently using AI. Chapters 00:00 - Introduction 00:47 - HorizonDB Architecture Overview 02:47 - Performance Demo 03:37 - AI Model Management 04:19 - VS Code Postgres Tools Overview 06:44 - Debugging Query Plans with GitHub Copilot 09:16 - AI Pipelines 14:32 - Wrap Up Recommended resources Learn Docs Azure Product page Connect Scott Hanselman | Twitter/X: @SHanselman Microsoft for PostgreSQL | LinkedIn: Microsoft for PostgreSQL Azure Friday | Twitter/X: @AzureFriday

Foundry Observability empowers developers to take agents from prototype to production with end-to-end observability. We demonstrate how evaluations, tracing, monitoring, and optimization work together to identify issues, measure quality, and improve outcomes over time. Through a live demo spanning the Foundry portal and VS Code, we showcase a practical workflow for building more reliable, production-ready agents. Chapters 00:00 - Introduction 01:33 - Foundry portal demo 06:58 - VS Code demo 17:43 - Wrap up Recommended resources Observability in Generative AI - Microsoft Foundry Build 2026 Resources Connect Scott Hanselman | Twitter/X: @SHanselman Azure Friday | Twitter/X: @AzureFriday

The new Azure file share experience is now generally available! In this episode of Azure Friday, we explore how the new management model simplifies the way you create and manage file shares, helping streamline your day-to-day operations. We also walk through the updated portal experience and highlight some of the key benefits of the new file share experience. Chapters 00:00 - Introduction 03:38 - Demo on portal 10:20 - Benefits 12:54 - Closing & learn more Recommended resources Learn Docs Azure Product page Blog post Connect Scott Hanselman | Twitter/X: @SHanselman Azure Friday | Twitter/X: @AzureFriday Azure | Twitter/X: @Azure