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

Transactions ensure database operations are reliable using ACID properties.

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

Event Sourcing stores every action as an event to recreate the current state.

Kafka consumer offsets help track message processing to avoid loss and duplication.

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

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.

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

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

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

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

Retention policies help manage how long data is kept in databases.

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

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

Time-series databases are designed to store and query time-stamped metrics efficiently.