How would you implement failure handling in a Kafka consumer application to ensure data is not lost during consumer replication?

In a Kafka consumer, failure handling is achieved by combining manual offset commits, idempotent writes, retries, and dead‑letter handling. First, use manual offset commits only after the record has been successfully processed and persisted to the downstream system. This guarantees that a consumer crash will not cause data loss because the offset is not committed until the write succeeds. Second, make the downstream write idempotent (e.g., use a unique key or a transactional API) so that re‑processing the same record does not corrupt data. Third, implement a retry loop with exponential back‑off for transient errors, and after a configurable number of attempts, route the record to a dead‑letter topic. Kafka’s transactional API can be used to bundle the consume‑commit and produce‑to‑dead‑letter into a single atomic operation, giving exactly‑once semantics. Finally, enable consumer group rebalancing and monitor lag; if a consumer dies, another instance will take over its partitions. By combining these techniques—manual commits, idempotent writes, retries, DLQ, and transactions—you can ensure that data is never lost and that the system recovers gracefully from failures.