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Transactions ensure database operations are reliable using ACID properties.

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

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

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

Optimistic and pessimistic locking are strategies to manage database access during concurrent transactions.

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

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

Kafka's commitSync waits for confirmation, while commitAsync continues immediately.

Database isolation levels determine how transactions interact and affect data consistency.

Logs, metrics, and traces help engineers debug issues in software systems.

Kafka uses state stores to remember previous events in stateful stream processing.

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

Kafka commit strategies determine how offsets are managed during message processing.

Write-Ahead Log ensures data is safely recorded before changes are made.