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

Kafka supports data scalability primarily through horizontal scaling by adding brokers and partitions. In Kafka, a broker is a server that stores data and serves client requests. When the load increases, Kafka allows the addition of more brokers to the cluster, which distributes the data and the processing workload more evenly across the system.

Partitions are fundamental to Kafka's design; they allow topics (the categories or feeds of data) to be split into smaller, more manageable pieces. Each partition can reside on a different broker, enabling parallel processing of data. This setup not only enhances performance but also provides fault tolerance, as data is replicated across multiple brokers. When a new broker is added, Kafka can redistribute partitions among the available brokers, effectively increasing both the storage and throughput capabilities of the system.

This approach to scalability distinguishes Kafka from systems that rely on a centralized database, which can become a bottleneck as demand grows. Unlike limiting data volume per partition, which may restrict performance and increase latency, the addition of brokers and partitions allows Kafka to handle vast amounts of data seamlessly. Increasing the number of client connections is also not a direct means of achieving scalability, as it does not directly address the underlying data storage and processing architecture.