CI/CD GitHub Deployment from Dev to UAT Synapse Workspace not Picking Up UAT Resources
Hello, I am setting up CI/CD for Azure Synapse Analytics using GitHub Actions with multiple environments (Dev, UAT, Prod). My Synapse resources are: Dev: ************-dev, azcalsbdatalakedev, calsbvaultdev, SQL DB azcalsbazuresqldev / MetaData UAT: ************-uat, azcalsbdatalakeuat, calsbvaultuat, SQL DB azcalsbazuresqluat / MetaData Prod: ***********-prod, azcalsbdatalakeprod, azcalsbvaultprod, SQL DB azcalsbazuresqlprod / MetaData I have environment-specific parameter override files like uat.json and prod.json. My GitHub workflows (synapse-dev.yml, synapse-uat.yml, etc.) deploy the Synapse publish artifacts (TemplateForWorkspace.json and TemplateParametersForWorkspace.json) with those overrides. Issue: When I run the UAT workflow, deployment completes successfully but the UAT Synapse workspace still shows Dev resources. For example, linked services like LS_ADLS still point to azcalsbdatalakedev instead of azcalsbdatalakeuat. What I have tried: Created overrides for UAT (uat.json) with correct workspace name and connection strings Checked GitHub workflow YAML to confirm the override file is being passed in the az deployment group create step Verified that Dev deployment works fine Tried changing default values in linked services JSON but behavior is inconsistent Questions: Is there a specific way to structure override files (uat.json) for Synapse CI/CD deployments so environment values are correctly replaced? Do I need separate branches in GitHub for Dev, UAT, and Prod, or can I deploy to all environments from main with overrides? Has anyone else seen linked services or parameters still pointing to Dev even after a UAT deployment? Any guidance, best practices, or sample YAML and override examples would be very helpful. Thanks in advance.Step-by-Step: How to Setup Copilot Chat in VS Code
Copilot Chat is an AI-powered chatbot leveraging OpenAI's GPT-4, designed to enhance your coding workflow. Learn how to set up Copilot Chat step by step in Visual Studio Code (VS Code). Benefit from personalized and flexible coding environments, code analysis, automated unit test generation, and bug fixes. Prerequisites include an active GitHub account and the latest version of VS Code. Elevate your coding efficiency to new heights with Copilot Chat.đ From Chessboard to Cloud: My Journey into Azure Security
Hi everyone đ I'm Perparim Abdullahu, an Azure Solutions Architect focused on Microsoft 365 & Azure security. I recently joined the Learn Community to share hands-on labs, visual guides, and real-world insights from my journey. I specialize in designing Zero Trust architectures using Microsoft Entra ID (CA, PIM), Intune, Defender, and Purview. Iâm certified in AZ-305, AZ-104, and SC-300, and currently preparing for SC-100. Through #PerparimLabs, Iâve built a growing archive of diagrams, KQL snippets, and âgotchasâ from the field. My goal is to turn complex deployments into clear, visual stories that help others learn faster. đ§ Coming soon: A visual breakdown of Insider Risk Management with Purview A lab on Conditional Access policies with real-world use cases Excited to learn from this community and contribute back. Letâs build secure, resilient systemsâtogether đ. #AzureSecurity | #ZeroTrust | #MicrosoftLearn| Microsoft Entra | Conditional Access | #PerparimLabs
microsoft learn programe
I also take courses on Microsoft Learn. In the videos and written tutorials, they explain how to do certain things â for example, in Power Automate. But often, the version of the program I have on my PC is not the same as the one shown in the videos or instructions. This makes learning difficult. It would be very helpful if Microsoft Learn clearly indicated which version of the software is being used in each tutorial or video.Model Mondays S2E9: Models for AI Agents
1. Weekly Highlights This episode kicked off with the top news and updates in the Azure AI ecosystem: GPT-5 and GPT-OSS Models Now in Azure AI Foundry: Azure AI Foundry now supports OpenAIâs GPT-5 lineup (including GPT-5, GPT-5 Mini, and GPT-5 Nano) and the new open-weight GPT-OSS models (120B, 20B). These models offer powerful reasoning, real-time agent tasks, and ultra-low latency Q&A, all with massive context windows and flexible deployment via the Model Router. Flux 1 Context Pro & Flux 1.1 Pro from Black Forest Labs: These new vision models enable in-context image generation, editing, and style transfer, now available in the Image Playground in Azure AI Foundry. Browser Automation Tool (Preview): Agents can now perform real web tasksâsearch, navigation, form filling, and moreâvia natural language, accessible through API and SDK. GitHub Copilot Agent Mode + Playwright MCP Server: Debug UIs with AI: Copilotâs agent mode now pairs with Playwright MCP Server to analyze, identify, and fix UI bugs automatically. Discord Community: Join the conversation, share your feedback, and connect with the product team and other developers. 2. Spotlight On: Azure AI Agent Service & Agent Catalog This weekâs spotlight was on building and orchestrating multi-agent workflows using the Azure AI Agent Service and the new Agent Catalog. What is the Azure AI Agent Service? A managed platform for building, deploying, and scaling agentic AI solutions. It supports modular, multi-agent workflows, secure authentication, and seamless integration with Azure Logic Apps, OpenAPI tools, and more. Agent Catalog: A collection of open-source, ready-to-use agent templates and workflow samples. These include orchestrator agents, connected agents, and specialized agents for tasks like customer support, research, and more. Demo Highlights: Connected Agents: Orchestrate workflows by delegating tasks to specialized sub-agents (e.g., mortgage application, market insights). Multi-Agent Workflows: Design complex, hierarchical agent graphs with triggers, events, and handoffs (e.g., customer support with escalation to human agents). Workflow Designer: Visualize and edit agent flows, transitions, and variables in a modular, no-code interface. Integration with Azure Logic Apps: Trigger workflows from 1400+ external services and apps. 3. Customer Story: Atomic Work Atomic Work showcased how agentic AI can revolutionize enterprise service management, making employees more productive and ops teams more efficient. Problem: Traditional IT service management is slow, manual, and frustrating for both employees and ops teams. Solution: Atomic Workâs âAtomâ is a universal, multimodal agent that works across channels (Teams, browser, etc.), answers L1/L2 questions, automates requests, and proactively assists users. Technical Highlights: Multimodal & Cross-Channel: Atom can guide users through web interfaces, answer questions, and automate tasks without switching tools. Data Ingestion & Context: Regularly ingests up-to-date documentation and context, ensuring accurate, current answers. Security & Integration: Built on Azure for enterprise-grade security and seamless integration with existing systems. Demo: Resetting passwords, troubleshooting VPN, requesting GitHub repo accessâall handled by Atom, with proactive suggestions and context-aware actions. Atom can even walk users through complex UI tasks (like generating GitHub tokens) by âseeingâ the userâs screen and providing step-by-step guidance. 4. Key Takeaways Here are the key learnings from this episode: Agentic AI is Production-Ready: Azure AI Agent Service and the Agent Catalog make it easy to build, deploy, and scale multi-agent workflows for real-world business needs. Modular, No-Code Workflow Design: The workflow designer lets you visually create and edit agent graphs, triggers, and handoffsâno code required. Open-Source & Extensible: The Agent Catalog provides open-source templates and welcomes community contributions. Real-World Impact: Solutions like Atomic Work show how agentic AI can transform IT, HR, and customer support, making organizations more efficient and employees more empowered. Community & Support: Join the Discord and Forum to connect, ask questions, and share your own agentic AI projects. Sharda's Tips: How I Wrote This Blog Writing this blog is like sharing my own learning journey with friends. I start by thinking about why the topic matters and how it can help someone new to Azure or agentic AI. I use simple language, real examples from the episode, and organize my thoughts with GitHub Copilot to make sure I cover all the important points. Hereâs the prompt I gave Copilot to help me draft this blog: Generate a technical blog post for Model Mondays S2E9 based on the transcript and episode details. Focus on Azure AI Agent Service, Agent Catalog, and real-world demos. Explain the concepts for students, add a section on practical applications, and share tips for writing technical blogs. Make it clear, engaging, and useful for developers and students. After watching the video, I felt inspired to try out these tools myself. The way the speakers explained and demonstrated everything made me believe that anyone can get started, no matter their background. My goal with this blog is to help you feel the same wayâcurious, confident, and ready to explore what AI and Azure can do for you. If you have questions or want to share your own experience, Iâd love to hear from you. Coming Up Next Week Next week: Document Processing with AI! Join us as we explore how to automate document workflows using Azure AI Foundry, with live demos and expert guests. 1ïžâŁ | Register For The Livestream â Aug 18, 2025 2ïžâŁ | Register For The AMA â Aug 22, 2025 3ïžâŁ | Ask Questions & View Recaps â Discussion Forum About Model Mondays Model Mondays is a weekly series designed to help you build your Azure AI Foundry Model IQ with three elements: 5-Minute Highlights â Quick news and updates about Azure AI models and tools on Monday 15-Minute Spotlight â Deep dive into a key model, protocol, or feature on Monday 30-Minute AMA on Friday â Live Q&A with subject matter experts from Monday livestream Want to get started? Register For Livestreams â every Monday at 1:30pm ET Watch Past Replays to revisit other spotlight topics Register For AMA â to join the next AMA on the schedule Recap Past AMAs â check the AMA schedule for episode specific links Join The Community Great devs don't build alone! In a fast-paced developer ecosystem, there's no time to hunt for help. That's why we have the Azure AI Developer Community. Join us today and let's journey together! Join the Discord â for real-time chats, events & learning Explore the Forum â for AMA recaps, Q&A, and Discussion! About Me I'm Sharda, a Gold Microsoft Learn Student Ambassador interested in cloud and AI. Find me on GitHub, Dev.to, Tech Community, and LinkedIn. In this blog series, I summarize my takeaways from each week's Model Mondays livestream.Fix Broken Migrations with AI Powered Debugging in VS Code Using GitHub Copilot
Data is at the heart of every application. But evolving your schema is risky business. One broken migration, and your dev or prod environment can go down. We've all experienced it: mismatched columns, orphaned constraints, missing fields, or that dreaded "table already exists" error. But what if debugging migrations didnât have to be painful? What if you could simply describe the error or broken state, and AI could fix your migration in seconds? In this blog, youâll learn how to: Use GitHub Copilot to describe and fix broken migrations with natural language Catch schema issues like incorrect foreign keys before they block your workflow Validate and deploy your database changes using GibsonAI CLI Broken migrations are nothing new. Whether you're working on a side project or part of a large team, itâs all too easy to introduce schema issues that can block deployments or corrupt local environments. Traditionally, fixing them means scanning SQL files, reading error logs, and manually tracking down what went wrong. But what if you could skip all that? What if you could simply describe the issue in plain English and AI would fix it for you? Thatâs exactly what GitHub Copilot let you do, right from within VS Code. What You Need: Visual Studio Code Installed Account in GitHub Sign up with GitHub Copilot GibsonAI CLI installed and logged in Letâs Break (and Fix) a Migration: Hereâs a common mistake. Say you create two tables: users and posts. CREATE TABLE users ( id UUID PRIMARY KEY, name TEXT, email TEXT UNIQUE ); CREATE TABLE posts ( id UUID PRIMARY KEY, title TEXT, user_id UUID REFERENCES user(id) ); The problem? The posts table refers to a table called user, but you named it users. This one-word mistake breaks the migration. If you've worked with relational databases, youâve probably run into this exact thing. Just Ask a GitHub Copilot: Instead of troubleshooting manually, open Copilot Chat and ask: âMy migration fails because posts.user_id references a missing user table. Can you fix the foreign key?â Copilot understands what you're asking. It reads the context and suggests the fix: CREATE TABLE posts ( id UUID PRIMARY KEY, title TEXT, user_id UUID REFERENCES users(id) ); It even explains what changed, so you learn along the way. Wait â how does Copilot know what I mean? GitHub Copilot is smart enough to understand your code, your errors, and even what youâre asking in plain English. It doesnât directly connect to GibsonAI. Youâll use the GibsonAI CLI for that, but Copilot helps you figure things out and fix your code faster. Validating with GibsonAI Once Copilot gives you the fixed migration, itâs time to test it. Run: gibson validate This checks your migration and schema consistency. When you're ready to apply it, just run: gibson deploy GibsonAI handles the rest so no broken chains, no surprises. Why This Works Manual debugging of migrations is frustrating and error prone. GibsonAI with GitHub Copilot: Eliminates guesswork in debugging You donât need to Google every error Reduces time to fix production schema issues You stay in one tool: VS Code You learn while debugging Whether you're a student learning SQL or a developer on a fast moving team, this setup helps you recover faster and ship safer. Fixing migrations used to be all trial and error, digging through files and hoping nothing broke. It was time-consuming and stressful. Now with GitHub Copilot and GibsonAI, fixing issues is fast and simple. Copilot helps you write and correct migrations. GibsonAI lets you validate and deploy with confidence. So next time your migration fails, donât panic. Just describe the issue to GitHub Copilot, run a quick check with GibsonAI, and get back to building. Ready to try it yourself? Sign up atgibsonai.com Want to Go Further? If youâre ready to explore more powerful workflows with GibsonAI, here are two great next steps: GibsonAI MCP Server â Enable Copilot Agent Mode to integrate schema intelligence directly into your dev environment. Automatic PR Creation for Schema Changes â The in-depth guide on how to automate pull requests for database updates using GibsonAI. Want to Know More About GitHub Copilot? Explore these resources to get the most out of Copilot: Get Started with GitHub Copilot Introduction to prompt engineering with GitHub Copilot GitHub Copilot Agent Mode GitHub Copilot Customization Use GitHub Copilot Agent Mode to create a Copilot Chat application in 5 minutes Deploy Your First App Using GitHub Copilot for Azure: A Beginnerâs Guide That's it, folks! But the best part?âŻYou can become part of a thriving community of learners and builders by joining theâŻMicrosoft Student Ambassadors Community.âŻConnect with like minded individuals, explore hands-on projects, and stay updated with the latest in cloud and AI. đŹ Join the community on DiscordâŻhere and explore more benefits on the Microsoft Learn Student Hub.What is GitHub Codespaces and how can Students access it for free?
GitHub Codespaces is a new service that is free for anyone to develop with powerful environments using Visual Studio Code. In this post, we'll cover how you can make use of this new technology and take advantage of its most powerful features.Create Stunning AI Videos with Sora on Azure AI Foundry!
Special credit to Rory Preddy for creating the GitHub resource that enable us to learn more about Azure Sora. Reach him out on LinkedIn to say thanks. Introduction Artificial Intelligence (AI) is revolutionizing content creation, and video generation is at the forefront of this transformation. OpenAI's Sora, a groundbreaking text-to-video model, allows creators to generate high-quality videos from simple text prompts. When paired with the powerful infrastructure of Azure AI Foundry, you can harness Sora's capabilities with scalability and efficiency, whether on a local machine or a remote setup. In this blog post, Iâll walk you through the process of generating AI videos using Sora on Azure AI Foundry. Weâll cover the setup for both local and remote environments. Requirements: Azure AI Foundry with sora model access A Linux Machine/VM. Make sure that the machine already has the package below: Java JRE 17 (Recommended) OR later Maven Step Zero â Deploying the Azure Sora model on AI Foundry Navigate to the Azure AI Foundry portal and head to the âModels + Endpointsâ section (found on the left side of the Azure AI Foundry portal) > Click on the âDeploy Modelâ button > âDeploy base modelâ > Search for Sora > Click on âConfirmâ. Give a deployment name and specify the Deployment type > Click âDeployâ to finalize the configuration. You should receive an API endpoint and Key after successful deploying Sora on Azure AI Foundry. Store these in a safe place because we will be using them in the next steps. Step one â Setting up the Sora Video Generator in the local/remote machine. Clone the roryp/sora repository on your machine by running the command below: git clone https://github.com/roryp/sora.git cd sora Then, edit the application.properties file in the src/main/resources/ folder to include your Azure OpenAI Credentials. Change the configuration below: azure.openai.endpoint=https://your-openai-resource.cognitiveservices.azure.com azure.openai.api-key=your_api_key_here If port 8080 is used for another application, and you want to change the port for which the web app will run, change the âserver.portâ configuration to include the desired port. Allow appropriate permissions to run the âmvnwâ script file. chmod +x mvnw Run the application ./mvnw spring-boot:run Open your browser and type in your localhost/remote host IP (format: [host-ip:port]) in the browser search bar. If you are running a remote host, please do not forget to update your firewall/NSG to allow inbound connection to the configured port. You should see the web app to generate video with Sora AI using the API provided on Azure AI Foundry. Now, letâs generate a video with Sora Video Generator. Enter a prompt in the first text field, choose the video pixel resolution, and set the video duration. (Due to technical limitation, Sora can only generate video of a maximum of 20 seconds). Click on the âGenerate videoâ button to proceed. The cost to generate the video should be displayed below the âGenerate Videoâ button, for transparency purposes. You can click on the âView Breakdownâ button to learn more about the cost breakdown. The video should be ready to download after a maximum of 5 minutes. You can check the status of the video by clicking on the âCheck Statusâ button on the web app. The web app will inform you once the download is ready and the page should refresh every 10 seconds to fetch real-time update from Sora. Once it is ready, click on the âDownload Videoâ button to download the video. Conclusion Generating AI videos with Sora on Azure AI Foundry is a game-changer for content creators, marketers, and developers. By following the steps outlined in this guide, you can set up your environment, integrate Sora, and start creating stunning AI-generated videos. Experiment with different prompts, optimize your workflow, and let your imagination run wild! Have you tried generating AI videos with Sora or Azure AI Foundry? Share your experiences or questions in the comments below. Donât forget to subscribe for more AI and cloud computing tutorials!Participe da 2ÂȘ edição do GitHub Copilot Global Bootcamp
O GitHub Copilot Global Bootcamp começou em fevereiro como uma jornada totalmente virtual de aprendizado â e foi um sucesso. Mais de 60 mil desenvolvedores participaram da primeira edição, em vĂĄrios idiomas e regiĂ”es. Agora, estamos empolgados em lançar a segunda edição â maior e melhor â com workshops virtuais e presenciais, organizados por comunidades de tecnologia ao redor do mundo. Essa nova edição chega logo apĂłs os anĂșncios do Microsoft Build 2025, onde as equipes do GitHub e do Visual Studio Code revelaram novidades empolgantes: A extensĂŁo GitHub Copilot Chat serĂĄ open source, reforçando a transparĂȘncia e a colaboração. A IA estĂĄ sendo profundamente integrada ao Visual Studio Code, que agora estĂĄ evoluindo para um editor de IA de cĂłdigo aberto. Novas APIs e ferramentas estĂŁo tornando mais fĂĄcil do que nunca construir com IA e LLMs. Este bootcamp Ă© a sua oportunidade de explorar essas novas ferramentas, entender como usar o GitHub Copilot de forma eficaz e fazer parte da crescente conversa global sobre IA no desenvolvimento de software. đ©âđ» Quem pode participar? Seja vocĂȘ um(a) desenvolvedor(a) iniciante, estudante ou profissional experiente em tecnologia, este bootcamp foi feito para vocĂȘ. VocĂȘ aprenderĂĄ casos de uso prĂĄticos do GitHub Copilot e como aumentar sua produtividade usando IA â em um formato acessĂvel e prĂĄtico. Participe da edição virtual NĂŁo importa onde vocĂȘ esteja, Ă© possĂvel participar online e aprender com a gente: PortuguĂȘs (Brasil â UTC -3) 24 de junho, 19h: Boas prĂĄticas para dominar o GitHub Copilot Chat 25 de junho, 19h: IntegraçÔes prĂĄticas com MCP Servers no VS Code e GitHub Copilot Aprenda na sua cidade! Estamos em parceria com comunidades locais de desenvolvedores para levar workshops presenciais a diversas cidades ao redor do mundo. SessĂ”es presenciais confirmadas no Brasil: Data Cidade InscriçÔes 17 de Junho BrasĂlia, Brasil Registre-se agora! 17 de Junho Pato de Minas, Brasil Registre-se agora! 21 de Junho Mogi das Cruzes, Brasil Registre-se agora! 26 de Junho Recife, Brasil Registre-se agora! 27 de Junho Rio de Janeiro, Brasil Registre-se agora! O Microsoft Applied Skills Ă© um programa de credenciamento criado para validar sua capacidade de realizar tarefas tĂ©cnicas especĂficas do mundo real. Diferente das certificaçÔes tradicionais que costumam abranger cargos amplos, o Applied Skills foca em habilidades prĂĄticas e cenĂĄrios reais, diretamente aplicĂĄveis a desafios de negĂłcios. E a melhor parte? Ă totalmente gratuito! VocĂȘ demonstra suas habilidades por meio de avaliaçÔes interativas, baseadas em tarefas, em um ambiente simulado â sem perguntas de mĂșltipla escolha, apenas trabalho real. Uma das adiçÔes mais recentes Ă© o Applied Skill do GitHub Copilot, que comprova sua habilidade de aproveitar a IA para aumentar a produtividade no desenvolvimento de software e melhorar a qualidade do cĂłdigo: Acelere o desenvolvimento de aplicativos usando o GitHub Copilot.
