/azure-architecture

name: azure-architecture
description: Expert guidance for designing Azure solutions using Azure Architecture. Covers reference architectures, solution ideas, design patterns, technology choices, architecture styles, best practices, anti-patterns, example workloads, and migration guides. Use when designing AKS/microservices, SAP on Azure, hybrid/Arc, GenAI/RAG, or mission-critical workloads, and other Azure Architecture related development tasks.
compatibility: Requires network access. Uses mcp_microsoftdocs:microsoft_docs_fetch or fetch_webpage to retrieve documentation.
metadata:
  generated_at: "2026-11-08"
  generator: "docs2skills/1.0.0"

Azure Architecture Skill

This skill provides expert guidance for designing Azure solutions using Azure Architecture. Covers reference architectures, solution ideas, design patterns, technology choices, architecture styles, best practices, anti-patterns, example workloads, and migration guides. It combines local quick-reference content with remote documentation fetching capabilities.

How to Use This Skill

> **IMPORTANT for Agent**: Use the **Category Index** below to locate relevant sections. For categories with line ranges (e.g., `L35-L120`), use `read_file` with the specified lines. For categories with file links (e.g., `[security.md](security.md)`), use `read_file` on the linked reference file

> **IMPORTANT for Agent**: If `metadata.generated_at` is more than 3 months old, suggest the user pull the latest version from the repository. If `mcp_microsoftdocs` tools are not available, suggest the user install it: [Installation Guide](https://github.com/MicrosoftDocs/mcp/blob/main/README.md)

This skill requires **network access** to fetch documentation content:

• **Preferred**: Use `mcp_microsoftdocs:microsoft_docs_fetch` with query string `from=learn-agent-skill`. Returns Markdown.
• **Fallback**: Use `fetch_webpage` with query string `from=learn-agent-skill&accept=text/markdown`. Returns Markdown.

Category Index

| Category | Lines | Description | |----------|-------|-------------| | Reference Architectures | L37-L90 | End-to-end Azure solution blueprints: reference designs for ML, data platforms, networking, security, hybrid/Arc, AKS/microservices, SAP, VMs, App Service, and mission-critical architectures. | | Solution Ideas | L91-L131 | End-to-end solution patterns for AI, data, security, and infrastructure on Azure—covering MLOps, multimodal AI, analytics, mainframe/SAP migration, DevSecOps, and multilayer defense designs. | | Design Patterns | L132-L182 | Patterns for resilient, scalable, and secure app design on Azure: messaging, integration, API gateways, data partitioning, caching, identity, migration, and failure isolation. | | Technology Choices | L183-L217 | Guides for choosing Azure/Fabric services (AI/ML, data, analytics, storage, compute, networking, containers, messaging, search) and matching them to specific workload needs. | | Architecture Styles | L218-L229 | Guidance on Azure architecture styles (big compute, big data, event-driven, microservices, N-tier, web-queue-worker) and how to design, choose, and implement them for cloud solutions. | | Best Practices | L230-L291 | Best-practice patterns for Azure apps: GenAI/RAG, REST APIs, autoscaling, multitenancy, networking, AKS, Event Hubs, SAP, DR, security, monitoring, and operations. | | Anti-patterns | L292-L306 | Diagnosing and fixing common cloud performance anti-patterns (busy tiers, chatty I/O, no caching, noisy neighbors, retry storms, sync I/O) and applying scalable Azure design practices. | | Example Workloads | L307-L393 | End-to-end reference architectures and implementation guides for real-world Azure workloads: data platforms, caching, AKS, hybrid networking, IoT, mainframe migration, security, DR, and enterprise apps. | | Migration Guides | L394-L424 | Guides for migrating from AWS/GCP/on-prem (Oracle, Kafka, Kubernetes/EKS) to Azure, mapping services, architectures, identity, security, networking, and cost/ops differences. |

Reference Architectures

| Topic | URL | |-------|-----| | Implement baseline Azure ML online inference platform | https://learn.microsoft.com/en-us/azure/architecture/ai-ml/architecture/baseline-azure-machine-learning-inference | | Deploy baseline enterprise chat with Microsoft Foundry | https://learn.microsoft.com/en-us/azure/architecture/ai-ml/architecture/baseline-microsoft-foundry-chat | | Implement baseline Foundry chat in an Azure landing zone | https://learn.microsoft.com/en-us/azure/architecture/ai-ml/architecture/baseline-microsoft-foundry-landing-zone | | Deploy MLOps v2 CI/CD and retraining architectures | https://learn.microsoft.com/en-us/azure/architecture/ai-ml/guide/machine-learning-operations-v2 | | Implement DR-ready Azure enterprise data platform architecture | https://learn.microsoft.com/en-us/azure/architecture/data-guide/disaster-recovery/dr-for-azure-data-platform-architecture | | Enterprise-hardened Azure Data Factory lakehouse architecture | https://learn.microsoft.com/en-us/azure/architecture/databases/architecture/azure-data-factory-enterprise-hardened | | Mission-critical Azure Data Factory analytics architecture | https://learn.microsoft.com/en-us/azure/architecture/databases/architecture/azure-data-factory-mission-critical | | Implement Azure Data Factory medallion lakehouse baseline | https://learn.microsoft.com/en-us/azure/architecture/databases/architecture/azure-data-factory-on-azure-landing-zones-baseline | | Deploy MongoDB Atlas securely on Azure | https://learn.microsoft.com/en-us/azure/architecture/databases/architecture/mongodb-atlas-baseline | | Move archive data from mainframes to Azure storage | https://learn.microsoft.com/en-us/azure/architecture/example-scenario/mainframe/move-archive-data-mainframes | | Deploy a hub-spoke AKS cluster with Azure Firewall | https://learn.microsoft.com/en-us/azure/architecture/guide/aks/aks-firewall | | Architect mission-critical global content delive