Apache Storm vs Structured 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 structured streaming when building real-time data pipelines, such as iot data ingestion, fraud detection, or live analytics dashboards, as it simplifies stream processing with familiar sql-like syntax. 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

Structured Streaming

Developers should learn Structured Streaming when building real-time data pipelines, such as IoT data ingestion, fraud detection, or live analytics dashboards, as it simplifies stream processing with familiar SQL-like syntax

Pros

• It's particularly useful in scenarios requiring low-latency processing with strong consistency guarantees, as it integrates seamlessly with existing Spark batch jobs and supports various data sources like Kafka, HDFS, and cloud storage
• Related to: apache-spark, spark-sql

Cons

• Specific tradeoffs depend on your use case

The Verdict

These tools serve different purposes. Apache Storm is a platform while Structured 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 Structured Streaming excels in its own space.