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

Point-in-Time Recovery allows databases to be restored to a specific moment before data loss.

Kafka uses retention to keep historical data and compaction to keep the latest state.

The Saga Pattern manages distributed transactions using local transactions and compensating actions.

Disaster Recovery Testing verifies that your recovery plan works effectively in real situations.

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

Late-arriving events can be dropped, updated, or routed based on system needs.

Distributed tracing helps track requests across microservices to find delays.

CDC with Kafka allows real-time updates from databases to multiple services.

The dual-write trap in Elasticsearch can cause data inconsistency during reindexing.

Watermarks help stream processing systems manage event time and late events.