Structured Streaming vs Kafka Streams

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 meets developers should learn kafka streams when building real-time data pipelines, event-driven architectures, or stream processing applications that require low-latency processing of high-volume data streams. Here's our take.

Structured Streaming

Nice Pick

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

Kafka Streams

Developers should learn Kafka Streams when building real-time data pipelines, event-driven architectures, or stream processing applications that require low-latency processing of high-volume data streams

Pros

• It is ideal for use cases like real-time analytics, fraud detection, monitoring systems, and data enrichment where data must be processed as it arrives, leveraging Kafka's durability and fault tolerance
• Related to: apache-kafka, stream-processing

Cons

• Specific tradeoffs depend on your use case

The Verdict

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

The Bottom Line

Structured Streaming wins

Based on overall popularity. Structured Streaming is more widely used, but Kafka Streams excels in its own space.