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Elasticsearch Data Streams simplify time-series data management with one name for many indices.

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

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

You can replay Kafka events safely by separating live and replay processes.

The Transactional Outbox Pattern ensures database updates and event publishing are synchronized to avoid losing events.

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

Kafka Streams uses local state for quick data access and changelogs for recovery.

Kafka consumer reprocessing involves safely replaying events to correct system states.