Can you compare and contrast Akka Streams with other popular streaming frameworks (e.g., Kafka Streams, Flink, or Spark Streaming)?
Akka Streams is a reactive stream processing library built on the Actor model, emphasizing backpressure and non-blocking I/O. It focuses on fine-grained control over resource usage and low-latency processing.
Kafka Streams is a lightweight library for building streaming applications atop Apache Kafka. It provides stateful processing, windowing, and joins while leveraging Kafka’s scalability and fault-tolerance capabilities.
Flink is a distributed stream processing framework with advanced features like event time processing, exactly-once semantics, and support for batch processing. It offers high throughput and low latency at scale.
Spark Streaming is an extension of Apache Spark that enables scalable, fault-tolerant stream processing. It processes data in micro-batches, providing near-real-time analytics and compatibility with Spark’s ecosystem.
