Apache Storm vs Apache Spark Streaming

Developers should learn Apache Storm when building applications that require real-time stream processing, such as real-time analytics, fraud detection, IoT data processing, or social media sentiment analysis 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.

Apache Storm

Nice Pick

Developers should learn Apache Storm when building applications that require real-time stream processing, such as real-time analytics, fraud detection, IoT data processing, or social media sentiment analysis

Pros

• It's particularly useful in scenarios where low-latency processing of continuous data streams is critical, and it integrates well with message queues like Kafka or RabbitMQ for data ingestion
• Related to: apache-kafka, apache-flink

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. Apache Storm is a platform while Apache Spark Streaming is a framework. We picked Apache Storm based on overall popularity, but your choice depends on what you're building.

The Bottom Line

Apache Storm wins

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