Conquer Apache Kafka 2025 – Dive into Data Streaming Dominance!

Kafka achieves fault tolerance primarily through message replication across multiple brokers. This mechanism ensures that each piece of data (or message) published to a Kafka topic is stored redundantly across different brokers in the Kafka cluster. When a producer sends a message to a topic, this message is not just written to one broker; it is replicated to a specified number of other brokers based on the configuration.

In the event that a broker goes down or becomes inaccessible, the messages can still be retrieved from one of the replicas on a different broker. This redundancy plays a critical role in maintaining the availability and durability of messages. It safeguards against data loss, as even if the primary broker that holds the original message fails, the replicated copies ensure that the messages are still available for consumption.

The other choices do not provide the same level of fault tolerance. Distributing messages across several consumers facilitates parallel processing and load balancing but does not protect against data loss. Compressing messages saves storage space but does not contribute to fault tolerance. Single broker redundancy would not be sufficient since it could still result in data loss if that one broker fails. Hence, message replication across multiple brokers is the cornerstone of Kafka's fault tolerance strategy.