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name: gcp-deployment-manager compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont
• config description: '"Infrastructure as code using YAML templates for repeatable GCP resource" deployments.' license: MIT maturity: stable metadata: domain: cncf output-format: manifests related-skills: azure-resource-manager, gcp-cloud-sql role: reference scope: infrastructure triggers: deployment manager, infrastructure as code, iac, templates, yaml, cloudformation, kubernetes deployment, container orchestration archetypes:
  • educational
  • strategic anti_triggers:
  • brainstorming
  • vague ideation
  • non-containerized architecture response_profile: verbosity: medium directive_strength: low abstraction_level: strategic version: "1.0.0"

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Google Cloud Deployment Manager

Deploy and manage google cloud deployment manager infrastructure as part of your cloud-native environment.

TL;DR Checklist

• Enable monitoring and logging
• Configure security and access controls
• Set up automated backups
• Enable high availability
• Implement disaster recovery
• Document configuration
• Test failover procedures
• Set up alerting

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When to Use

Use google cloud deployment manager when:

• Running cloud infrastructure workloads
• Requiring managed services from the platform
• Needing automatic scaling and high availability
• Integrating with other cloud services
• Building cloud-native applications

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When NOT to Use

Avoid google cloud deployment manager when:

• Self-managed alternatives better suit your requirements
• Cost optimization strongly favors different solutions
• Specific vendor lock-in concerns exist
• Custom implementations provide better control

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Purpose and Use Cases

Primary Purpose: Provide cloud infrastructure for modern application deployments.

Common Use Cases:

1. Production Deployments — Running enterprise workloads
2. Development Environments — Quick provisioning for testing
3. Disaster Recovery — Backup and failover infrastructure
4. Scaling Applications — Auto-scaling for varying loads
5. Data Processing — Batch and stream processing jobs

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Architecture Design Patterns

Pattern 1: High Availability Configuration

# Reference: Configure for high availability
# - Enable redundancy across availability zones
# - Set up automatic failover
# - Configure load balancing
# - Enable automated backups

Pattern 2: Security Hardening

# Reference: Implement security best practices
# - Enable encryption at rest and in transit
# - Configure network security controls
# - Implement identity and access management
# - Enable audit logging

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Integration Approaches

1. Integration with Monitoring

Enable comprehensive observability:

• Real-time metrics collection
• Log aggregation and analysis
• Performance monitoring
• Alert configuration

2. Integration with Security

Implement defense-in-depth:

• Identity and access management
• Encryption at rest and in transit
• Network security controls
• Compliance monitoring

3. Integration with Other Services

Connect with platform services:

• Load balancing and traffic management
• Database services
• Storage solutions
• Container orchestration

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Common Pitfalls

❌ Pitfall 1: Missing Monitoring

Problem: No visibility into resource health and performance.

Solution:

• Enable comprehensive monitoring
• Set up log aggregation
• Configure alerting rules
• Review metrics regularly

❌ Pitfall 2: Inadequate Security

Problem: Resources exposed to unauthorized access.

Solution:

• Enforce identity and access management
• Enable encryption
• Configure network security groups
• Conduct security audits

❌ Pitfall 3: No Backup Strategy

Problem: Data loss with no recovery option.

Solution:

• Enable automated backups
• Test restore procedures
• Store backups in separate regions
• Document recovery processes

❌ Pitfall 4: Single Point of Failure

Problem: Service outage from single resource failure.

Solution:

• Enable high availability features
• Distribute across availability zones
• Implement automatic failover
• Design for graceful degradation

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Best Practices Summary

Category	Best Practice
Availability	Enable high availability and auto-scaling
Security	Enforce IAM, encryption, and network controls
Monitoring	Enable comprehensive observability
Backups	Automated backups with regular testing
Cost	Right-size resources and monitor spending

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Related Skills

Skill	Purpose
cncf-gcp-compute-engine	Related infrastructure service
cncf-gcp-vpc	Related infrastructure service
cncf-gcp-cloud-sql	Related infrastructure service
cncf-gcp-gke	Related infrastructure service

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Core Workflow

1. Assess Requirements — Understand the use case, scale, integration needs, and existing infrastructure. Checkpoint: Document requirements, constraints, and success criteria.

2. Design Architecture — Plan component interactions, data flow, and deployment strategy using cloud-native best practices. Checkpoint: Verify the architecture addresses all requirements and follows CNCF conventions.

3. Implement & Configure — Create manifests, configurations, and deployment scripts. Include resource limits, health checks, and observability hooks. Checkpoint: Validate all YAML against schema and test in a staging environment.

4. Deploy & Monitor — Apply manifests to the cluster, verify component health, and confirm observability is working. Checkpoint: Confirm all pods/services are running, probes passing, and metrics/alerts configured.

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Constraints

MUST DO

• Include at least one complete working YAML manifest example
• Note when content is auto-generated vs. manually verified
• Reference relevant CNCF project documentation

MUST NOT DO

• Deploy manifests without testing in a staging environment first
• Use deprecated API versions (e.g., apps/v1beta1)
• Omit resource limits and requests in Kubernetes manifests

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Live References

Authoritative documentation links for this skill's domain. The model follows markdown links at load time to resolve external references and inline content.

• Primary Documentation
• API Reference or Getting Started
• Configuration Guide
• Best Practices
• Common Patterns or Tutorials