real-time-streaming

To handle late data in Flink stream processing, use watermarks and windowing configurations. Watermarks track event time progress, allowing window operators to wait for delayed records before finalizing computations and materializing results to downstream sinks.

Exactly-once processing in real-time streaming prevents duplicate events and data loss by coordinating transactions across components. It requires idempotent producers, transactional sinks, and consistent state management to guarantee reliable pipeline operation despite failures.

Manage backpressure and failure recovery in Kafka stream processing by configuring appropriate state management and checkpointing practices. This prevents runaway state growth and brittle pipeline failures, ensuring robust and reliable operation during throughput spikes.

Flink and Kafka Streams both support stateful windowing and joins for stream processing, but Flink offers advanced event time handling and watermarks. You can integrate them within your streaming architecture to balance processing capabilities and operational complexity.