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Kafka consumers can fetch data in batches to improve throughput and reduce latency.

Older offsets can overwrite newer ones in Kafka if not handled properly.

Kafka's `seek()` lets you replay only specific messages, saving time.

Kafka Streams uses tumbling and hopping windows for time-based data aggregation.

Kafka uses state stores to remember previous events in stateful stream processing.

Kafka Streams offers higher-level abstractions, while Kafka Consumer provides direct control.

Kafka's commitSync waits for confirmation, while commitAsync continues immediately.

Backpressure prevents system overload by managing message processing rates in Kafka.

Eager rebalancing revokes all assignments, while cooperative allows incremental changes.