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

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

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

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

Timeouts in distributed systems indicate suspicion, not confirmed failure.

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

Vertical scaling means upgrading one server, while horizontal scaling means adding more servers.

Consistency models determine what data reads in distributed systems can see.

Distributed backpressure helps manage the flow of work between services to prevent overload.

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

A Circuit Breaker helps prevent system failures by stopping calls to unhealthy services.

Exponential backoff helps manage retries by increasing wait times to reduce system overload.

Kafka can help ensure messages are processed once, but requires careful design.