Apache Kafka's core strength lies in its robust producer-consumer model, enabling decoupled, scalable, and fault-tolerant real-time data streaming. This model forms the backbone of many modern data pipelines, allowing applications to publish and subscribe to streams of records.

Producers are applications that publish (write) records to Kafka topics. A record consists of a key, a value, and a timestamp. Producers can choose to send records to specific partitions within a topic or let Kafka decide based on the record's key. Keyed records are guaranteed to be delivered to the same partition, ensuring order for a given key.

Consumers are applications that subscribe to (read) records from one or more Kafka topics. They process these records in a distributed and fault-tolerant manner. Kafka consumers operate within consumer groups.

A consumer group is a set of consumers that work together to consume a topic. Kafka assigns partitions of a topic to the consumers within a group. Each partition is consumed by at most one consumer within a given consumer group. This ensures that each record is processed only once by a consumer group. If multiple consumers are in the same group, they share the partitions of the subscribed topics. If consumers are in different groups, they each receive a full copy of the data.

Consumers keep track of which records they have processed using offsets. An offset is a unique, sequential identifier for each record within a partition. Kafka consumers commit their offsets to Kafka periodically. This allows a consumer to resume processing from where it left off if it crashes or restarts, preventing data loss or duplicate processing.

The interaction is straightforward: producers write data to topics, and consumers read data from topics. Kafka acts as the intermediary, storing the data durably and making it available to consumers. This decoupling allows producers and consumers to operate independently, at different rates, and to be scaled independently.