Catalog
concept#Reliability#Architecture#Observability#Security

Disaster Recovery

Strategies, processes and technical measures to restore IT systems and data after major outages or disasters.

Disaster recovery defines strategies, processes and technologies to restore IT systems and data after major outages.
Established
High

Classification

  • High
  • Technical
  • Architectural
  • Intermediate

Technical context

Backup and restore systems (e.g. Veeam, Bacula)Replication and storage solutionsMonitoring and incident management tools

Principles & goals

Define clear RTO and RPO targets per business process.Automate failover and recovery where it makes sense.Conduct regular tests and validation of DR procedures.
Run
Enterprise, Domain

Use cases & scenarios

Compromises

  • Missing or outdated runbooks cause recovery delays.
  • Insufficiently tested backups may be unusable in an incident.
  • Cost pressure may lead to insufficient investment in redundancy.
  • Conduct regular DR tests in realistic scenarios.
  • Derive RTO/RPO from business priorities.
  • Use automation for consistent and repeatable recovery steps.

I/O & resources

  • Inventory of critical systems and dependencies
  • Current backup and replication configurations
  • Communication and escalation plans
  • Tested recovery runbooks
  • Restored systems and validated data
  • Post-incident report and improvement actions

Description

Disaster recovery defines strategies, processes and technologies to restore IT systems and data after major outages. The goal is to minimize downtime (RTO) and data loss (RPO) through planning, backups, replication and tested recovery procedures. It covers organizational processes, technical measures and regular validation tests.

✔

  • Reduced downtime and faster recovery of critical services.
  • Minimized data loss through defined RPO strategies.
  • Increased organizational resilience and incident responsiveness.

✖

  • Significant financial and operational effort for redundancy and testing.
  • Complexity in heterogeneous or legacy system landscapes.
  • Not all scenarios can be fully automated.

  • RTO (Recovery Time Objective)

    Maximum tolerable time to restore a service.

  • RPO (Recovery Point Objective)

    Maximum tolerable data loss in time (time since last consistent backup).

  • Mean Time To Recovery (MTTR)

    Average time required to fully recover after an outage.

Bank — data center DR test

Annual DR exercise with failover to a secondary site and measurement of service recovery times.

E‑commerce — ransomware recovery

Recovery after an encryption attack using point-in-time backups and rebuild of affected systems.

SaaS provider — multi-region failover

Automated failover between cloud regions to minimize visible downtime for customers.

1

Analyze critical services and define RTO/RPO.

2

Design and implement redundancy and replication architecture.

3

Create runbooks and automation scripts.

4

Perform regular tests and drills and adapt processes.

⚠️ Technical debt & bottlenecks

  • Legacy systems without modern replication mechanisms.
  • Missing automation for recurring recovery steps.
  • Insufficient documentation of recovery processes.
Single point of failureNetwork bandwidth for replicationBackup retention and storage capacity
  • Infrequent, incomplete tests lead to false confidence.
  • Copying old backups without validating integrity.
  • Testing failover only in maintenance windows instead of production-like conditions.
  • Ignoring dependencies between systems when planning recovery.
  • Overestimating backup availability without recovery tests.
  • Not considering compliance requirements when selecting sites.
System and infrastructure administrationNetwork and storage knowledgeIncident planning and incident management experience
RTO and RPO requirements of business processesRegulatory requirements and complianceCost and budget constraints for redundancy
  • • Budget constraints for redundant sites
  • • Legacy systems without native replication
  • • Legal or data protection constraints for site selection