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kappa_architecture

streaming only

  • Kappa Architecture simplifies Lambda by eliminating the batch layer.
  • Instead of separate batch & streaming paths, Kappa uses only a single stream-processing system.

How It Works​

  1. All data is processed as a real-time stream.
  2. Stream processors (e.g., Apache Flink, Kafka Streams, Spark Streaming) process data and store results in a queryable system (e.g., Delta Lake, Elasticsearch, Apache Pinot).
  3. If historical data needs to be reprocessed, the system simply replays the event logs.

Pros & Cons​

ProsCons
✅ Simpler than Lambda – No need to maintain separate batch and streaming layers❌ Not optimized for massive historical batch processing
✅ Lower latency – Streaming means instant insights❌ Reprocessing historical data requires replaying event logs
✅ Easier to maintain – No need to merge batch & real-time results❌ May require more compute resources for real-time processing

When to Use Kappa Architecture?​

  • When real-time streaming is the primary use case.
  • When historical batch processing is not a priority.
  • When working with event-driven architectures (e.g., real-time analytics, fraud detection, IoT data).

References