The service read data from Kafka, enriched it with data from Redis — and then what did it do?

After the service reads data from Kafka and enriches it with information from Redis, the next steps usually depend on the business logic:

• Data processing and transformation: for example, filtering, aggregation, or calculating new metrics.
• Writing to target systems: data can be sent to databases, other message queues, or external APIs.
• Sending events: publishing enriched messages to another Kafka topic for further processing.
• Monitoring and logging: recording processing results and errors for support and analysis.

Example: the service receives user action events from Kafka, enriches them with profile data from Redis, then sends the final data to Elasticsearch for analytics and visualization.