Lambda Architecture vs Kappa Architecture

Compare Lambda and Kappa architectures for real-time analytics.

Lambda Architecture

Maintains both a Batch Layer and a Speed Layer; results are merged from both.

• Batch Layer: Periodically (e.g., hourly) processes full historical data, producing accurate but delayed results.
• Speed Layer: Processes recent data in real-time to fill the batch layer delay gap.
• Serving Layer: Merges both results for queries.

Pros: Accurate batch results with real-time supplementation.

Cons: Two codebases (batch + streaming) with duplicated logic and operational complexity.

Kappa Architecture

Eliminates the batch layer; uses a single streaming system (typically Kafka + Flink) for everything. Historical reprocessing is done by replaying Kafka messages.

Pros: Single code path, simpler to operate.

Cons: Streaming system must handle massive historical reprocessing; some complex batch operations are hard to implement in streaming.

Modern Trend

Kappa is gaining popularity for its simplicity. Kafka long retention and Flink capability make it viable. Delta Lake/Iceberg unified streaming+batch also reduces the need for Lambda.