How does cloud-native DR differ from traditional DR?

Cloud-native disaster recovery (DR) differs from traditional DR primarily in infrastructure, automation, and scalability. Traditional DR relies on physical hardware, such as secondary data centers with replicated servers and storage, often requiring manual processes to restore systems from backups. In contrast, cloud-native DR leverages cloud infrastructure, distributed services, and automation to enable faster recovery with minimal downtime. For example, while traditional DR might involve shipping backup tapes to a remote site and manually rebuilding servers, cloud-native DR can automatically spin up resources in another region using pre-configured templates and real-time data replication.

A key technical difference lies in the tools and services used. Traditional DR often depends on periodic backups, physical hardware redundancy, and scripts that require human intervention. For instance, restoring a database might involve manually applying transaction logs from a backup server. Cloud-native DR, however, uses managed services like AWS Aurora Global Database or Azure Site Recovery, which automate replication and failover. Kubernetes clusters in the cloud can self-heal by restarting failed pods across zones, and infrastructure-as-code tools like Terraform can reprovision entire environments in minutes. This automation reduces recovery time objectives (RTO) by eliminating manual steps, such as configuring network settings or installing software.

Cost and scalability models also differ significantly. Traditional DR requires upfront investment in redundant hardware, which sits idle until a disaster occurs, leading to high capital expenses. Cloud-native DR operates on a pay-as-you-go model, where resources are provisioned dynamically during a disaster, reducing idle costs. For example, a cloud-native application might use auto-scaling groups to add server capacity during recovery, while traditional setups would need permanently reserved servers. Additionally, cloud-native architectures often design for failure by distributing workloads across regions, enabling granular recovery of microservices instead of monolithic systems. This approach minimizes downtime and data loss (improving RPO) compared to traditional methods that may restore entire systems from hours-old backups.