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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.

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.

Timeouts in distributed systems indicate suspicion, not confirmed failure.

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

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

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

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

Primary shards partition data, while replica shards duplicate it for redundancy.