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Disaster recovery ensures systems can restore services and data after major disruptions.

Disaster Recovery Testing verifies that your recovery plan works effectively in real situations.

Elasticsearch CCR allows real-time data replication for faster disaster recovery.

Replicas keep your system available, while snapshots allow for data recovery.

Point-in-Time Recovery allows databases to be restored to a specific moment before data loss.

Active-passive architecture ensures a standby system takes over if the main system fails.

Global failover ensures applications remain available by redirecting traffic from failed regions to functioning ones.

Dependency failures can disrupt applications, but resilience patterns can help manage them.

High Availability ensures your application remains operational even during server failures.

Data residency is crucial for compliance with laws about where data can be stored.

Database replication helps keep data available and allows systems to handle more read requests.

Fault domains help prevent multiple servers from failing together in an architecture.

Fallbacks allow systems to handle failures safely and maintain user experience.

Cascading failures occur when one service's failure impacts others, causing widespread issues.

Partial failure means some services fail while others keep running, impacting system reliability.

N+1 redundancy means having one extra server to maintain capacity during a failure.

Kafka uses retries and Dead Letter Queues to manage message processing failures.

A single component's failure can take down your entire application, even with redundancy.

Database replication enhances data availability and read performance but has challenges like replication lag.

CDC with Kafka allows real-time updates from databases to multiple services.

Kafka uses replication and in-sync replicas to ensure data is always available.

Retry storms occur when too many retries overload a failing service.

Kafka allows safe message reprocessing by resetting consumer positions without altering data.

The retry pattern helps systems recover from temporary failures but can cause overload if misused.