Kafka Streams vs Spark Streaming: Which is Better?

What are the main differences between Kafka Streams and Spark Streaming?

Kafka Streams is a lightweight library for processing data in real-time, while Spark Streaming is a more comprehensive framework that can handle batch and stream processing. Kafka Streams is tightly integrated with Kafka, making it ideal for applications that rely on Kafka for data ingestion.

Which one is easier to set up and use?

Kafka Streams is generally easier to set up, especially for applications already using Kafka. It requires less configuration and can be embedded directly in your application. Spark Streaming, on the other hand, may require a more complex setup with a Spark cluster.

Can you give me an example use case for each?

Sure! A typical use case for Kafka Streams is real-time analytics on user activity data, like tracking clicks on a website. For Spark Streaming, a common use case is processing log data from multiple sources to generate insights or alerts.

Which one performs better under heavy load?

Performance can vary based on the specific use case and architecture. Generally, Kafka Streams can handle high throughput with low latency due to its lightweight nature, while Spark Streaming may introduce some latency due to its micro-batch processing model.