Architecture

Official Documentation

Getting Started

  1. 8/28/2017, Mva
    This technology-agnostic course begins by explaining the benefits of distributed cloud applications with an emphasis on maintaining high-availability and scalability in a...
  2. 12/8/2017, Ebook
    This guide presents a structured approach for designing cloud applications that are scalable, resilient, and highly available. The guidance in this ebook is intended to help...
  3. 2/10/2017, Video, 0:56:41
    One of the benefits of cloud technology that is only just being realised is the greatly reduced risk profile and timelines it enables when designing and delivering robust,...
  4. 9/23/2015, Ebook
    This guide contains twenty-four design patterns and ten related guidance topics that articulate the benefits of applying patterns by showing how each piece can fit into the...
  5. 12/8/2017, Ebook
    We created The Developer’s Guide to Microsoft Azure to help you on your journey to the cloud, whether you’re just considering making the move, or you’ve already decided and...
  6. 5/20/2016, Ebook
    This guide breaks down the “why” and “how” for scenarios suited to the cloud with a focus on building apps using platform services available in Microsoft Azure. The second...



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Azure Documentation

1. Azure Architecture Center
2. Cloud Fundamentals
     2.1. Application architecture guide
          2.1.1. Introduction
          2.1.2. Architecture styles
               2.1.2.1. Overview
               2.1.2.2. N-tier application
               2.1.2.3. Web-queue-worker
               2.1.2.4. Microservices
               2.1.2.5. CQRS
               2.1.2.6. Event-driven architecture
               2.1.2.7. Big data
               2.1.2.8. Big compute
          2.1.3. Choosing a compute service
               2.1.3.1. Overview
               2.1.3.2. Decision tree
               2.1.3.3. Compute comparison
          2.1.4. Choosing a data store
               2.1.4.1. Overview
               2.1.4.2. Data store comparison
          2.1.5. Design principles
               2.1.5.1. Overview
               2.1.5.2. Design for self-healing
               2.1.5.3. Make all things redundant
               2.1.5.4. Minimize coordination
               2.1.5.5. Design to scale out
               2.1.5.6. Partition around limits
               2.1.5.7. Design for operations
               2.1.5.8. Use managed services
               2.1.5.9. Use the best data store for the job
               2.1.5.10. Design for evolution
               2.1.5.11. Build for the needs of business
          2.1.6. Pillars of software quality
          2.1.7. Best practices for cloud architectures
               2.1.7.1. API design
               2.1.7.2. API implementation
               2.1.7.3. Autoscaling
               2.1.7.4. Background jobs
               2.1.7.5. Caching
               2.1.7.6. Content Delivery Network
               2.1.7.7. Data partitioning
               2.1.7.8. Monitoring and diagnostics
               2.1.7.9. Naming conventions
               2.1.7.10. Transient fault handling
               2.1.7.11. Retry guidance for specific services
     2.2. Data architecture guide
          2.2.1. Introduction
          2.2.2. Traditional RDBMS workloads
               2.2.2.1. Overview
               2.2.2.2. Online transaction processing (OLTP)
               2.2.2.3. Data warehousing
               2.2.2.4. Online analytical processing (OLAP)
               2.2.2.5. Extract, transform, and load (ETL)
          2.2.3. Big data architectures
               2.2.3.1. Overview
               2.2.3.2. Batch processing
               2.2.3.3. Real time processing
               2.2.3.4. Machine learning at scale
               2.2.3.5. Non-relational data stores
               2.2.3.6. Scenarios
                    2.2.3.6.1. Advanced analytics
                    2.2.3.6.2. Data lakes
                    2.2.3.6.3. Free-form text search
                    2.2.3.6.4. Interactive data exploration
                    2.2.3.6.5. Natural language processing
                    2.2.3.6.6. Time series solutions
                    2.2.3.6.7. Working with CSV and JSON files
               2.2.3.7. Technology choices
                    2.2.3.7.1. Analytical data stores
                    2.2.3.7.2. Analytics and reporting
                    2.2.3.7.3. Batch processing
                    2.2.3.7.4. Cognitive services
                    2.2.3.7.5. Data storage
                    2.2.3.7.6. Machine learning
                    2.2.3.7.7. Natural language processing
                    2.2.3.7.8. Pipeline orchestration
                    2.2.3.7.9. Real-time message ingestion
                    2.2.3.7.10. Search data stores
                    2.2.3.7.11. Stream processing
          2.2.4. Cross-cutting concerns
               2.2.4.1. Data transfer
               2.2.4.2. Extending on-premises data solutions to the cloud
               2.2.4.3. Securing data solutions
     2.3. Cloud design patterns
          2.3.1. Overview
          2.3.2. Categories
               2.3.2.1. Availability
               2.3.2.2. Data management
               2.3.2.3. Design and implementation
               2.3.2.4. Messaging
               2.3.2.5. Management and monitoring
               2.3.2.6. Performance and scalability
               2.3.2.7. Resiliency
               2.3.2.8. Security
          2.3.3. Ambassador
          2.3.4. Anti-corruption Layer
          2.3.5. Backends for Frontends
          2.3.6. Bulkhead
          2.3.7. Cache-Aside
          2.3.8. Circuit Breaker
          2.3.9. Command and Query Responsibility Segregation (CQRS)
          2.3.10. Compensating Transaction
          2.3.11. Competing Consumers
          2.3.12. Compute Resource Consolidation
          2.3.13. Event Sourcing
          2.3.14. External Configuration Store
          2.3.15. Federated Identity
          2.3.16. Gatekeeper
          2.3.17. Gateway Aggregation
          2.3.18. Gateway Offloading
          2.3.19. Gateway Routing
          2.3.20. Health Endpoint Monitoring
          2.3.21. Index Table
          2.3.22. Leader Election
          2.3.23. Materialized View
          2.3.24. Pipes and Filters
          2.3.25. Priority Queue
          2.3.26. Queue-Based Load Leveling
          2.3.27. Retry
          2.3.28. Scheduler Agent Supervisor
          2.3.29. Sharding
          2.3.30. Sidecar
          2.3.31. Static Content Hosting
          2.3.32. Strangler
          2.3.33. Throttling
          2.3.34. Valet Key
     2.4. Performance antipatterns
          2.4.1. Overview
          2.4.2. Busy Database
          2.4.3. Busy Front End
          2.4.4. Chatty I/O
          2.4.5. Extraneous Fetching
          2.4.6. Improper Instantiation
          2.4.7. Monolithic Persistence
          2.4.8. No Caching
          2.4.9. Synchronous I/O
     2.5. Build microservices on Azure
          2.5.1. Introduction
          2.5.2. Domain analysis
          2.5.3. Identifying microservice boundaries
          2.5.4. Data considerations
          2.5.5. Interservice communication
          2.5.6. API design
          2.5.7. Ingestion and workflow
          2.5.8. API gateways
          2.5.9. Logging and monitoring
          2.5.10. CI/CD
     2.6. Manage multitenant identity
          2.6.1. Introduction
          2.6.2. The Tailspin scenario
          2.6.3. Authentication
          2.6.4. Claims-based identity
          2.6.5. Tenant sign-up
          2.6.6. Application roles
          2.6.7. Authorization
          2.6.8. Secure a web API
          2.6.9. Cache access tokens
          2.6.10. Client assertion
          2.6.11. Protect application secrets
          2.6.12. Federate with a customer's AD FS
          2.6.13. Run the Surveys application
     2.7. Migrate from Cloud Services to Service Fabric
          2.7.1. Migrate a Cloud Services application to Service Fabric
          2.7.2. Refactor a Service Fabric application
3. Example Scenarios
     3.1. Overview
     3.2. AI
          3.2.1. Image classification
     3.3. Apps
          3.3.1. Hotel reservation chatbot
          3.3.2. DevOps with Containers
          3.3.3. DevOps with VSTS
          3.3.4. SAP for Dev/Test
          3.3.5. E-commerce front-end
     3.4. Data & Analytics
          3.4.1. Real-time fraud detection
          3.4.2. Scalable order processing
     3.5. Infrastructure
          3.5.1. Multi-tier Windows
          3.5.2. HPC video rendering
4. Reference Architectures
     4.1. Overview
     4.2. App Service web application
          4.2.1. Basic web application
          4.2.2. Improved scalability
          4.2.3. Multi-region deployment
     4.3. Big data
          4.3.1. Enterprise BI with SQL Data Warehouse
          4.3.2. Automated enterprise BI with SQL Data Warehouse and Data Factory
     4.4. Hybrid networks
          4.4.1. Overview
          4.4.2. VPN
          4.4.3. ExpressRoute
          4.4.4. ExpressRoute with VPN failover
          4.4.5. Hub-spoke topology
          4.4.6. Hub-spoke topology with shared services
     4.5. Identity management
          4.5.1. Overview
          4.5.2. Integrate on-premises AD with Azure AD
          4.5.3. Extend AD DS to Azure
          4.5.4. Create an AD DS forest in Azure
          4.5.5. Extend AD FS to Azure
     4.6. N-tier application
          4.6.1. N-tier application with SQL Server
          4.6.2. Multi-region N-tier application
          4.6.3. N-tier application with Cassandra
          4.6.4. Deploy a Linux VM
          4.6.5. Deploy a Windows VM
     4.7. Network DMZ
          4.7.1. DMZ between Azure and on-premises
          4.7.2. DMZ between Azure and the Internet
          4.7.3. Highly available network virtual appliances
     4.8. SAP
          4.8.1. SAP NetWeaver for AnyDB
          4.8.2. SAP S/4HANA
          4.8.3. SAP HANA on Azure Large Instances
     4.9. Jenkins server
     4.10. SharePoint Server 2016
5. Design Review Framework
     5.1. Design for resiliency
     5.2. Failure mode analysis
     5.3. Availability checklist
     5.4. DevOps checklist
     5.5. Resiliency checklist (general)
     5.6. Resiliency checklist (Azure services)
     5.7. Scalability checklist
6. Cloud Adoption Guide
     6.1. Introduction
     6.2. Foundational Azure adoption
          6.2.1. Overview
          6.2.2. About Azure internals
               6.2.2.1. How does Azure work?
               6.2.2.2. What is cloud resource governance?
          6.2.3. About Azure resource access governance
               6.2.3.1. Understanding resource access management in Azure
               6.2.3.2. Azure governance design guide
          6.2.4. Deploying basic architectures to Azure
               6.2.4.1. Platform as a Service (PaaS) architecture
                    6.2.4.1.1. Deploy a basic web application to Azure
                    6.2.4.1.2. Best practices for web applications in Azure
               6.2.4.2. Infrastructure as a service (IaaS)
                    6.2.4.2.1. What is an Azure virtual network?
                    6.2.4.2.2. Deploy a virtual network to Azure
                    6.2.4.2.3. Deploy a single Windows VM IaaS workload to Azure
                    6.2.4.2.4. Best practices for deploying a Windows VM to Azure
                    6.2.4.2.5. Deploy a single Linux VM IaaS workload to Azure
                    6.2.4.2.6. Best practices for deploying a Linux VM to Azure
     6.3. Intermediate Azure adoption
          6.3.1. Overview
          6.3.2. Governance design guide
7. Azure Virtual Datacenter
8. Azure for AWS Professionals
     8.1. Overview
     8.2. Services comparison

Online Training Content

Date Title
8/28/2017 Architecting Distributed Cloud Applications
1/13/2015 Architecting Microsoft Azure Solutions

Tools

Tool Description

Videos

Date Title Length
5/6/2018
How Microsoft AI defeated Ms Pacman
1:33:22
5/6/2018
Inside Azure Datacenter Architecture with Mark Russinovich
1:33:22
4/26/2018
Containers and Existing .NET Apps Part 2
0:26:54
4/19/2018
Containers for Existing .NET Apps Part 1
0:29:52
12/18/2017
Enable Edge Computing with Azure IoT Edge
1:01:00
12/12/2017
Cloud Tech 10 - 11th December 2017 - Kubernetes, cloud-init, Cloud Foundry and more!
0:07:22
11/21/2017
Cybersecurity Reference Architecture & Strategies: How to Plan for and Implement a Cybersecurity Strategy
0:33:49
9/28/2017
Implement microservices patterns with .NET Core and Docker containers
0:46:21
9/27/2017
Patterns, Architecture, & Best Practices: Scaling Machine Learning Algorithms with Azure
1:09:20
9/21/2017
Deployment Techniques and Linux Containers with .NET Core 2.0
0:53:32

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