How do different stream processing frameworks compare?

What are the main differences between Apache Kafka and Apache Flink?

Apache Kafka is primarily a distributed messaging system, while Apache Flink is a stream processing framework that can process data in real-time. Kafka focuses on high-throughput and durability, whereas Flink provides advanced features like event time processing and stateful computations.

How does Spark Streaming fit into this comparison?

Spark Streaming is another popular framework that allows for micro-batch processing of streams. Unlike Flink, which processes data in real-time, Spark Streaming processes data in small batches, which can introduce latency.

Which framework would you recommend for a real-time analytics application?

For real-time analytics, Apache Flink is often recommended due to its low latency and powerful event time processing capabilities. However, if you're already using Kafka, integrating it with Spark Streaming can also be effective.

Are there any other frameworks worth considering?

Yes, other frameworks like Apache Storm and Google Cloud Dataflow are also worth considering, depending on your specific use case and infrastructure.