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What's new
Fundamentals
Content overview
Well-Architected Framework
Overview
What's new
Pillars
Operational excellence
Overview
Ensure operational readiness and performance using CloudOps
Manage incidents and problems
Manage and optimize cloud resources
Automate and manage change
Continuously improve and innovate
View on one page
Security, privacy, and compliance
Overview
Implement security by design
Implement zero trust
Implement shift-left security
Implement preemptive cyber defense
Use AI securely and responsibly
Use AI for security
Meet regulatory, compliance, and privacy needs
Shared responsibility and shared fate
View on one page
Reliability
Overview
Define reliability based on user-experience goals
Set realistic targets for reliability
Build high availability through redundancy
Take advantage of horizontal scalability
Detect potential failures by using observability
Design for graceful degradation
Perform testing for recovery from failures
Perform testing for recovery from data loss
Conduct thorough postmortems
View on one page
Cost optimization
Overview
Align spending with business value
Foster a culture of cost awareness
Optimize resource usage
Optimize continuously
View on one page
Performance optimization
Overview
Plan resource allocation
Take advantage of elasticity
Promote modular design
Continuously monitor and improve performance
View on one page
Sustainability
View all the pillars on one page
Cross-pillar perspectives
AI and ML
Overview
Operational excellence
Security
Reliability
Cost optimization
Performance optimization
View on one page
Financial services industry (FSI)
Overview
Operational excellence
Security
Reliability
Cost optimization
Performance optimization
View on one page
Deployment archetypes
Overview
Zonal
Regional
Multi-regional
Global
Hybrid
Multicloud
Comparative analysis
What's next
Reference architectures
Single-zone deployment on Compute Engine
Regional deployment on Compute Engine
Multi-regional deployment on Compute Engine
Global deployment on Compute Engine and Spanner
Landing zone design
Landing zones overview
Decide identity onboarding
Decide resource hierarchy
Network design
Decide network design
Implement network design
Decide security
Enterprise foundations blueprint
Overview
Architecture
Authentication and authorization
Organization structure
Networking
Detective controls
Preventative controls
Deployment methodology
Operations best practices
Deploy the blueprint
AI and machine learning
Content overview
Agentic AI
Overview
Choose agentic architecture components
Choose an agent design pattern
Multi-agent AI system
Single-agent AI system using ADK and Cloud Run
Use cases
Administer interactive learning
Automate data science workflows
Orchestrate access to disparate systems
Generative AI
Generative AI with RAG
Overview
Private connectivity for RAG-capable generative AI applications
RAG infrastructure using Gemini Enterprise and Vertex AI
RAG infrastructure using Vertex AI and Vector Search
RAG infrastructure using Vertex AI and AlloyDB
RAG infrastructure using Vertex AI and Cloud SQL
RAG infrastructure using GKE and Cloud SQL
GraphRAG infrastructure using Vertex AI and Spanner Graph
Harness CI/CD pipeline for RAG applications
Deploy an enterprise generative AI and ML model
Deploy and operate generative AI applications
Use cases
Automate utilization-review of health insurance claims
Build a knowledge base
Generate personalized marketing campaigns
Generate personalized product recommendations
Generate podcasts from audio
Generate solutions for customer support questions
Summarize documents on demand
ML applications and operations
Overview
Best practices for implementing ML on Google Cloud
Guidelines for high-quality, predictive ML solutions
MLOps using TensorFlow Extended, Vertex AI Pipelines, and Cloud Build
MLOps: Continuous delivery and automation pipelines in machine learning
Build an ML vision analytics solution with Dataflow and Cloud Vision API
Confidential computing for data analytics and AI
Cross-silo and cross-device federated learning
Implement two-tower retrieval with large-scale candidate generation
Model development and data labeling with Labelbox
Use Vertex AI Pipelines for propensity modeling
C3 AI architecture
AI and ML infrastructure
Design storage for AI and ML workloads in Google Cloud
Optimize AI and ML workloads with Cloud Storage FUSE
Optimize AI and ML workloads with Managed Lustre
Application development
Content overview
Development approaches and styles
Patterns for scalable and resilient apps
Development platform management
Deploy an enterprise developer platform
Overview
Architecture
Developer platform controls
Service architecture
Logging and monitoring
Operations
Costs and attributions
Deployment methodology
Cymbal Bank example
Mapping BeyondProd principles
Deploy the blueprint
Best practices for cost-optimized Kubernetes applications on GKE
Expose service mesh applications through GKE Gateway
Reference architecture
Deploy the architecture
Build globally distributed applications using GKE Gateway and Cloud Service Mesh
Reference architecture
Deploy the architecture
Patterns and practices for identity and access governance on Google Cloud
Resource management with ServiceNow
Select a managed container runtime environment
DevOps and development lifecycle
Architecture decision records overview
Develop and deliver apps with a deployment pipeline
Reference architecture
Deploy the architecture
DevOps Research and Assessment (DORA) capabilities
Application architectures
Apache Guacamole on GKE and Cloud SQL
Reference architecture
Deploy the architecture
Chrome Remote Desktop on Compute Engine
Set up for Linux
Set up for Windows
Connected device architectures on Google Cloud
Overview
Standalone MQTT broker
IoT platform product
Device to Pub/Sub connection to Google Cloud
Best practices for running an IoT backend
Best practices for automatically provisioning and configuring edge and bare metal systems and servers
Ecommerce platform with serverless computing
Manage and scale networking for Windows applications that run on managed Kubernetes
Reference architecture
Deploy the architecture
Dynamic web application with Python and JavaScript
Use a Cloud SDK Client Library
Three-tier web app
Website hosting
Big data and analytics
Content overview
End-to-end architectures
Analytics lakehouse
Import data into a secured BigQuery data warehouse
Data mesh on Google Cloud
Architecture and functions in a data mesh
Design a self-service data platform for a data mesh
Build data products in a data mesh
Discover and consume data products in a data mesh
Enterprise data management and analytics platform
Data warehouse with BigQuery
BigQuery backup automation
Reference architecture
Deploy the architecture
Load and process data
Continuous data replication to BigQuery using Striim
Analyze data
Data science with R: exploratory data analysis
Databases
Content overview
Oracle workloads
Overview
Enterprise application with Oracle Database on Compute Engine
Enterprise application on Compute Engine with Oracle Exadata
Oracle E-Business Suite with Oracle Database on Compute Engine
Oracle E-Business Suite on Compute Engine with Oracle Exadata
Oracle PeopleSoft on Compute Engine with Oracle Exadata
Multi-cloud database management
Hybrid and multicloud
Content overview
Build hybrid and multicloud architectures
Overview
Drivers, considerations, strategy, and patterns
Plan a hybrid and multicloud strategy
Architectural approaches to adopt a hybrid or multicloud architecture
Other considerations
What's next
View the guide as a single page
Hybrid and multicloud architecture patterns
Overview
Distributed architecture patterns
Tiered hybrid pattern
Partitioned multicloud pattern
Analytics hybrid and multicloud patterns
Edge hybrid pattern
Environment hybrid pattern
Business continuity hybrid and multicloud patterns
Cloud bursting pattern
What's next
View the guide as a single page
Hybrid and multicloud secure networking architecture patterns
Overview
Design considerations
Architecture patterns
Mirrored pattern
Meshed pattern
Gated patterns
Gated egress
Gated ingress
Gated egress and gated ingress
Handover pattern
General best practices
What's next
View the guide as a single page
Cross-Cloud Network for distributed applications
Overview
Connectivity
Service networking
Network security
Cross-Cloud Network inter-VPC connectivity using Network Connectivity Center
Cross-Cloud Network inter-VPC connectivity with VPC Network Peering
VPC Network Peering Cross-Cloud Network with NVAs and regional affinity
Hybrid and multicloud applications
Hybrid render farm
Build a hybrid render farm
Patterns for connecting other cloud service providers with Google Cloud
Identity and access management
Authenticate workforce users in a hybrid environment
Overview
Implementation patterns
Configure Active Directory for VMs to automatically join a domain
Deploy an Active Directory forest on Compute Engine
Patterns for using Active Directory in a hybrid environment
Third-party product integrations
Data management with Cohesity Helios and Google Cloud
Migration
Content overview
Migrate to Google Cloud
Get started
Assess and discover your workloads
Plan and build your foundation
Transfer your large datasets
Deploy your workloads
Migrate from manual deployments to automated, containerized deployments
Optimize your environment
Best practices for validating a migration plan
Minimize costs
Migrate from AWS to Google Cloud
Get started
Migrate Amazon EC2 to Compute Engine
Migrate Amazon S3 to Cloud Storage
Migrate Amazon EKS to GKE
Migrate from Amazon RDS and Amazon Aurora for MySQL to Cloud SQL for MySQL
Migrate from Amazon RDS and Amazon Aurora for PostgreSQL to Cloud SQL and AlloyDB for PostgreSQL
Migrate from Amazon RDS for SQL Server to Cloud SQL for SQL Server
Migrate from AWS Lambda to Cloud Run
Migrate from Azure to Google Cloud
Get started
Migrate to a Google Cloud VMware Engine platform
Application migration
Migrate containers to Google Cloud
Migrate from Kubernetes to GKE
Migrate across Google Cloud regions
Get started
Design resilient single-region environments on Google Cloud
Architect your workloads
Prepare data and batch workloads for migration across regions
Data and Database migration
Database migration guide
Concepts, principles, and terminology
