Apache Spark Streaming vs Apache Storm

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 meets 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. Here's our take.

Apache Spark Streaming

Nice Pick

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

Apache Storm

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

The Verdict

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

The Bottom Line

Apache Spark Streaming wins

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