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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.

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

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

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

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

Kafka uses Dead Letter Topics to handle messages that repeatedly fail processing.

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

Message queues allow services to communicate asynchronously, improving scalability and reliability.

Choosing the right boundaries for microservices is crucial for effective architecture.

Distributed locks ensure only one server performs a task, avoiding duplication.

Token Bucket allows burst traffic, while Leaky Bucket smooths out traffic flow.

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

The TCP 3-way handshake ensures reliable connection establishment between a client and server.