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

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

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

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

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

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

Jitter helps spread out client retries to avoid traffic spikes.

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

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

Timeouts in distributed systems indicate suspicion, not confirmed failure.