Apache Kafka Complete Practice Exam 2025 – All-in-One Prep

Kafka ensures data consistency primarily by delegating all requests through a single appointed leader for each partition in a topic. This architecture ensures that all writes and updates occur in a serialized manner, as every request must first go through the leader before any replicas can be updated.

When a producer sends data to a Kafka topic, it communicates exclusively with the leader of the relevant partition. Only after the leader has confirmed the write does it propagate the change to the follower replicas. This controlled flow of data avoids the potential for conflicting updates that could arise if multiple nodes were allowed to accept writes simultaneously. Consequently, this mechanism helps maintain a consistent state across all replicas, as they all replicate the leader's state directly.

In this setup, the leader acts as a source of truth, acknowledging the writes only after ensuring that all required replicas have received the update, further reinforcing data consistency. This methodology effectively mitigates issues such as data loss or duplicate messages, which can occur in distributed systems where multiple nodes might be independently accepting writes.