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There are several situations where Apache Kafka is preferred over ActiveMQ:

1. High throughput requirements: Apache Kafka is designed to handle high-speed data streams and can handle a large volume of messages per second. Hence, if your use case involves very high throughput requirements, Apache Kafka is the way to go.

2. Big Data integration: Apache Kafka is used as a messaging platform for Big Data integration, to collect, process, and store data across multiple systems. It provides a scalable platform for data processing and can easily integrate with other Big Data systems like Hadoop, Spark, and Flink.

3. Fault-tolerant system: Apache Kafka is designed to be highly available and fault-tolerant. It provides features like replication, partitioning, and fault tolerance to ensure data reliability and high availability.

4. Real-time processing: Apache Kafka is ideal for real-time processing of streaming data, where data is processed as soon as it is received. It is used in scenarios like event streaming, real-time reporting, and analytics.

5. Large-scale distributed systems: Apache Kafka is well suited for large-scale distributed systems, as it is easy to install, scale, and manage. It makes it easy to deploy and manage distributed systems that handle large volumes of data.