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

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

Fail-stop means a system stops and stays down, while fail-recover means it can come back but needs to be ready.

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

Kafka consumers resume processing from the last committed offset after a crash.

Kafka consumer reprocessing involves safely replaying events to correct system states.

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

Kafka Partition Reassignment moves replicas between brokers while keeping data safe.

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

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

Graceful degradation allows apps to function partially during service failures.

You can replay Kafka events safely by separating live and replay processes.

2-Phase Commit helps maintain data consistency in transactions across multiple databases.

The CAP Theorem explains the trade-offs in distributed systems during network failures.

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