In Kafka, messages are always stored using key value format, with key being the one used for determining the partition after hashing and value the actual data.

During Writing(message creation), producers uses serializers to convert the messages to bytes format. Kafka employs different kind of serializer based on the datatype which needs to be converted to byte format
Consumers uses deserizliser at their end to convert bytes to original data. Pro

It also allows custom serializer which helps in converting data to byte stream.

How Consumer reads data
Consumer keeps track of data read by having Consumer Offsets. A consumer offset in Kafka is a unique integer that tracks the position of the last message a consumer has processed in a partition
in order to “checkpoint” how far a consumer has been reading into a topic partition, the consumer will regularly commit the latest processed message, also known as consumer offset.

Offsets are important for a number of reasons, including: Data continuity: Offsets allow consumers to resume processing from where they left off if the stream application fails or shuts down.
Sequential processing: Offsets enable Kafka to process data in a sequential and ordered manner. Replayability: Offsets allow for replayable data processing.

When a consumer group is first initialized, consumers usually start reading from the earliest or latest offset in each partition. Consumers commit the offsets of messages they have processed successfully.
The position of the last available message in a partition is called the log-end offset. Consumers can store processed offsets in local variables or in-memory data structures, and then commit them in bulk.
Consumers can use a commit API to gain full control over offsets.