Apache Flink™ vs Apache Kafka™ Streams vs Apache Spark™ Structured Streaming — Comparing Stream Processing Engines
Blog post from Onehouse
Streaming engines are critical for processing continuous data streams in real-time or near real-time, serving applications that require low latency, high throughput, and scalability. The blog discusses the intricacies of streaming engines, highlighting key aspects like stateful processing, checkpointing, time semantics, backpressure handling, and delivery guarantee semantics, which are essential for robust fault tolerance and efficient data processing. Apache Flink and Kafka Streams are explored in depth due to their popularity, with Flink offering a high-throughput, low-latency engine ideal for comprehensive stateful operations, while Kafka Streams, integrated with Kafka clusters, is suited for building lightweight, event-driven applications. Spark Structured Streaming is noted for its simplicity and integration within the Apache Spark ecosystem, making it a top choice for rapid development with minimal setup. The piece emphasizes that a single engine may not suffice for all streaming needs, advocating for the use of multiple engines to balance performance and cost as workloads evolve.
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