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Read-after-write consistency ensures users see their latest updates immediately.

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

The Transactional Outbox Pattern ensures database updates and event publishing are synchronized to avoid losing events.

Raft's safety rules guarantee that committed entries are preserved even after leader failures.

Idempotency ensures that repeated operations in APIs do not cause duplicate effects.

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

Message delivery semantics define how messages are sent in distributed systems and handle failures.

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

Older offsets can overwrite newer ones in Kafka if not handled properly.

Graceful degradation allows apps to function partially during service failures.

Transactions ensure database operations are reliable using ACID properties.