Maintenance de bases de données multi-cloud

Effectue la maintenance, l'optimisation et la sauvegarde de bases de données dans des environnements multi-cloud. Utilisez-le pour garantir les performances et la santé des bases de données.

Spar Skills Guide Bot
DevOpsAvancé
1024/07/2026
Claude CodeCursorCopilot
#database-maintenance#multi-cloud#automation#backup#optimization

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name: maintain-databases description: Performs database maintenance, optimization, and backup procedures across multi-cloud environments. Use when ensuring database performance, managing database health, or implementing maintenance schedules. license: AGPLv3 metadata: author: agentic-reconciliation-engine version: "1.0" category: enterprise risk_level: medium autonomy: conditional layer: temporal compatibility: Requires Python 3.8+, cloud provider CLI tools (AWS CLI, Azure CLI, gcloud), and access to multi-cloud monitoring systems allowed-tools: Bash Read Write Grep

Database Maintenance — Multi-Cloud Enterprise Automation

Purpose

Enterprise-grade automation solution for database maintenance operations across AWS, Azure, GCP, and on-premise environments to maximize operational efficiency while maintaining security and compliance standards.

When to Use

  • database maintenance operations across multi-cloud environments
  • Automation and optimization of database maintenance workflows
  • Monitoring and management of database maintenance resources
  • Compliance and governance for database maintenance activities

Inputs

  • operation: Operation type (required)
  • targetResource: Target resource identifier (required)
  • cloudProvider: Cloud provider - aws|azure|gcp|onprem|all (optional, default: all)
  • parameters: Operation-specific parameters (optional)
  • environment: Target environment (optional, default: production)
  • dryRun: Dry run mode (optional, default: true)

Process

  1. Cloud Provider Detection: Identify target cloud providers and environments
  2. Input Validation: Comprehensive parameter validation and security checks
  3. Multi-Cloud Context Analysis: Analyze current state across all providers
  4. Operation Planning: Generate optimized execution plan
  5. Safety Assessment: Risk analysis and impact evaluation across providers
  6. Execution: Perform operations with monitoring and validation
  7. Results Analysis: Process results and generate reports

Outputs

  • Operation Results: Detailed execution results and status per provider
  • Compliance Reports: Validation and compliance status across environments
  • Performance Metrics: Operation performance and efficiency metrics by provider
  • Recommendations: Optimization suggestions and next steps
  • Audit Trail: Complete operation history for compliance across all providers

Environment

  • AWS: EKS, EC2, Lambda, CloudWatch, IAM, S3
  • Azure: AKS, VMs, Functions, Monitor, Azure AD
  • GCP: GKE, Compute Engine, Cloud Functions, Cloud Monitoring
  • On-Premise: Kubernetes clusters, VMware, OpenStack, Prometheus
  • Multi-Cloud Tools: Terraform, Ansible, Crossplane, Cluster API

Dependencies

  • Python 3.8+: Core execution environment
  • Cloud SDKs: boto3, azure-sdk, google-cloud
  • Kubernetes: kubernetes client for cluster operations
  • Multi-Cloud Libraries: terraform-python, ansible-python

Scripts

  • core/scripts/automation/database-maintenance.py: Main automation implementation
  • core/scripts/automation/database-maintenance_handler.py: Cloud-specific operations
  • core/scripts/automation/multi_cloud_orchestrator.py: Cross-provider coordination

Trigger Keywords

database, maintenance, automation, enterprise, multi-cloud, aws, azure, gcp, onprem

Human Gate Requirements

  • Production changes: Production environment operations require approval
  • High-impact operations: Critical operations require review
  • Security changes: Security modifications need validation

Enterprise Features

  • Multi-tenant Support: Isolated operations per tenant
  • Role-based Access Control: Enterprise IAM integration
  • Audit Logging: Complete audit trail for compliance
  • Performance Monitoring: SLA tracking and metrics
  • Security Hardening: Encryption and compliance standards
  • Dynamic Code Generation: Agents can modify logic dynamically
  • Cross-Cloud Orchestration: Coordinated operations across providers

Best Practices

  • Idempotent Operations: Safe retry mechanisms
  • Circuit Breaker Patterns: Resilience against failures
  • Rate Limiting: Respect API limits and implement backpressure
  • Graceful Degradation: Fallback strategies when providers are unavailable
  • Comprehensive Testing: Integration tests and compliance validation
  • Security First: Zero-trust architecture and principle of least privilege
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