Apache Kafka is primarily used as a distributed messaging system that excels in handling high-throughput data streams with strong durability guarantees. It's ideal for scenarios where you need a reliable backbone for event-driven architectures or data integration pipelines. For example, if you have multiple microservices that need to communicate asynchronously, using Kafka as a central message broker would be beneficial.
Apache Flink is ideal for scenarios requiring complex stream processing with state management and event time handling. You would use Flink when you need to analyze streams in real-time with operations like joins, aggregations, and windowed functions. For example, if you are building a real-time analytics platform that calculates user behavior metrics over time, Flink would be more suitable due to its powerful stateful processing and windowing capabilities.