Flink and Kafka Streams are two popular frameworks for real-time streaming, each with distinct architectural differences affecting scalability, state management, and operational complexity. Flink generally offers more flexibility and better state handling through its use of watermarks and remote storage, whereas Kafka Streams, being a library, simplifies integration but places greater operational burdens on developers. Ultimately, the choice between them depends on specific project requirements and team capabilities.

Flink 2.1 introduces DeltaJoin and MultiJoin, revolutionary join operators designed to tackle the excessive state management challenges in streaming applications. By externalizing state retrieval and leveraging Apache Fluss's efficient prefix lookup capabilities, these innovations aim to improve checkpoint efficiency and scalability, addressing the long-standing issues of traditional streaming joins. The article also contrasts Flink's approach with alternatives like RisingWave and Feldera, highlighting different philosophies in handling streaming state and joins.