Have you worked with Kafka?

Apache Kafka is a distributed event streaming platform designed for high-throughput, fault‑tolerant, and low‑latency data pipelines. It stores streams of records in topics, which are partitioned and replicated across brokers for scalability and resilience. Producers publish messages to topics, while consumers subscribe to topics and read messages in order. Key concepts include:

• Topics & Partitions – Topics are logical streams; partitions allow parallelism and ordering guarantees within each partition.

• Brokers & Clusters – Brokers are servers that host partitions; a cluster is a set of brokers working together.

• Replication – Each partition has a leader and follower replicas to ensure durability.

• Consumer Groups – Consumers in a group share the load; each message is delivered to one consumer in the group.

Typical use cases are real‑time analytics, log aggregation, event sourcing, and microservice communication. Kafka’s API supports both push‑style (consumer polling) and pull‑style (producer sending). Understanding its architecture, configuration (e.g., retention, compression), and integration patterns (e.g., Kafka Streams, ksqlDB) is fundamental for building scalable data pipelines.