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

Kafka consumers can use multiple threads to speed up message processing, but it complicates ordering and offset management.

Kafka's parallel processing depends on partitions, not just consumers or threads.

Kafka can help ensure messages are processed once, but requires careful design.

Backpressure helps manage message flow in Kafka during traffic spikes.

Kafka Schema Evolution allows safe changes to message structures without breaking existing consumers.

Kafka allows safe message reprocessing by resetting consumer positions without altering data.

Kafka uses retries and Dead Letter Queues to manage message processing failures.

Classifying failures and using controlled retries in Kafka prevents outages.

Kafka consumer lag shows how far behind consumers are from new messages.

Monitoring Kafka consumer lag helps prevent production problems by ensuring consumers keep up with message traffic.

Graceful shutdown in Kafka ensures no data loss during consumer termination.

Kafka uses retention to keep historical data and compaction to keep the latest state.

Kafka's pause temporarily stops reading messages, while seek changes the reading position.