How does Kafka scale?

Kafka scales horizontally by partitioning data within topics. Each partition can be processed by a separate broker, allowing load distribution across multiple servers. When the load increases, new brokers can be added and partitions can be redistributed among them.

Additionally, consumers in a group can read data from different partitions in parallel, increasing read throughput. Thus, scaling is achieved through:

• Adding brokers to the cluster
• Dividing topics into partitions
• Parallel processing of partitions by consumers

This enables Kafka to efficiently handle large volumes of data and maintain high performance.