Akka Streams vs Apache Spark Streaming

Developers should use Akka Streams when building scalable, resilient, and responsive applications that require processing large volumes of data streams, such as real-time analytics, IoT data pipelines, or microservices communication meets developers should learn apache spark streaming for building real-time analytics applications, such as fraud detection, iot sensor monitoring, or social media sentiment analysis, where low-latency processing of continuous data streams is required. Here's our take.

Akka Streams

Nice Pick

Developers should use Akka Streams when building scalable, resilient, and responsive applications that require processing large volumes of data streams, such as real-time analytics, IoT data pipelines, or microservices communication

Pros

• It is ideal for scenarios demanding back-pressure management to prevent overload, asynchronous processing to improve throughput, and composable stream graphs for complex transformations
• Related to: akka, reactive-streams

Cons

• Specific tradeoffs depend on your use case

Apache Spark Streaming

Developers should learn Apache Spark Streaming for building real-time analytics applications, such as fraud detection, IoT sensor monitoring, or social media sentiment analysis, where low-latency processing of continuous data streams is required

Pros

• It is particularly valuable in big data environments due to its integration with the broader Spark ecosystem, allowing seamless combination of batch and streaming workloads and leveraging Spark's in-memory computing for performance
• Related to: apache-spark, apache-kafka

Cons

• Specific tradeoffs depend on your use case

The Verdict

These tools serve different purposes. Akka Streams is a library while Apache Spark Streaming is a framework. We picked Akka Streams based on overall popularity, but your choice depends on what you're building.

The Bottom Line

Akka Streams wins

Based on overall popularity. Akka Streams is more widely used, but Apache Spark Streaming excels in its own space.