High availability and disaster recovery

Grand Central iPaaS provides enterprise-grade high availability and disaster recovery capabilities for critical banking operations. The platform minimizes downtime and data loss through automated recovery and self-healing infrastructure.

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Service level agreements

Grand Central offers tiered availability commitments: Recovery commitments:

• Recovery Time Objective (RTO): 6 hours for nominal situations
• Recovery Point Objective (RPO): Varies by data criticality; critical financial data uses synchronous replication for minimal data loss

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High availability architecture

The platform uses redundant infrastructure and automated recovery to maintain continuous operation during failures.

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Multi-zone deployment

The platform deploys services across multiple Azure availability zones within each region:

• Availability zones: Each zone operates independently with isolated power, cooling, and networking.
• Container distribution: Kubernetes pods spread across zones using anti-affinity rules and zone-aware scheduling.
• Data redundancy: Databases and storage replicate synchronously across zones for automatic failover without data loss.
• Network resilience: Load balancers distribute traffic across zones and reroute automatically when zones become unhealthy.
• Active-active-active deployment: Services run across three availability zones. The platform remains operational if two zones fail, provided the surviving zone has sufficient capacity to handle the workload.

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Auto-scaling and load balancing

The platform automatically adjusts capacity based on demand:

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Health monitoring

Kubernetes probes ensure application health:

• Readiness probes: Check if pods are ready to receive traffic.
• Liveness probes: Restart unhealthy containers automatically.
• Startup probes: Allow extended initialization time for slow-starting containers.

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Fault tolerance patterns

The platform implements patterns to prevent cascade failures:

• Circuit breakers: Detect failing services and fail fast to protect the system.
• Exponential backoff: Retry with increasing delays to prevent retry storms.
• Fallback services: Serve cached responses when primary services are unavailable.
• Dead letter queues: Route permanently failed requests for later analysis.

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GitOps and self-healing

The applications-live repository acts as the single source of truth for your runtime environment. ArgoCD continuously monitors cluster state:

• Drift detection: ArgoCD detects when cluster resources deviate from Git configuration, such as manual changes.
• Automated sync: The SelfHeal feature automatically reverts the cluster to the desired state defined in Git.
• Configuration reload: Reloader watches for ConfigMap and Secret changes and performs rolling restarts to apply new configurations.

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Observability stack

The platform includes a pre-configured monitoring stack. Access is controlled through roles assigned in the self-service.tfvars file.

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Grafana

Visualization dashboards for cluster health, performance metrics, and business logic.

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Additional observability tools

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Disaster recovery strategy

The platform implements multi-region failover to ensure business continuity during regional outages.

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Cross-region replication

Data and services replicate across multiple Azure regions for geographic redundancy:

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Data replication patterns

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Backup strategy

Backup storage uses geo-redundant storage with encryption at rest, immutable storage, and long-term retention.

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Failover procedures

Failover triggers include:

• Critical failures: Region outages, database corruption, security breaches
• Performance degradation: Response time thresholds, error rate spikes, SLA breaches
• Planned maintenance: Scheduled updates, infrastructure upgrades

The failover process executes through coordinated phases:

1. Detection: Automated monitoring identifies issues
2. Assessment: Evaluate scope and determine recovery strategy
3. Redirection: Update DNS and load balancer configurations
4. Validation: Health checks verify recovered services

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Recovery testing

The platform also uses chaos engineering to inject controlled failures and validate resilience.

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Incident management

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Incident classification

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Escalation procedures

• Automated escalation: Time-based rules, severity-based routing, on-call integration
• Communication protocols: Stakeholder notifications, status page updates, customer communication

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Regulatory compliance

The platform supports banking regulatory requirements for business continuity:

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Next steps