Big Data Architectures: Lambda and Kappa Explained

Big data teams design pipelines to handle large volumes and fast streams. Two common patterns are Lambda and Kappa. This article explains what each pattern does, when to choose one, and what trade-offs you should expect.

Lambda architecture

Lambda uses three layers to balance accuracy and timeliness. The batch layer stores raw events and recomputes comprehensive views on a schedule. The speed (streaming) layer processes new events quickly to give low-latency results. The serving layer makes the latest batch and stream views available for queries.

  • Batch layer: immutable logs of data and periodic batch views
  • Speed layer: low-latency results from recent events
  • Serving layer: combines views for fast, up-to-date answers

Pros: robust fault tolerance, full audit trails, and resilience to missing or late data. Cons: more complexity, extra storage, and potential data duplication across layers.

Kappa architecture

Kappa simplifies the stack by using a single log stream for all data. Every event flows through a streaming processor, and results are served directly from the stream processing state. If a data issue appears, the stream can be replayed from the log to rebuild results.

  • Single stream path reduces maintenance
  • Reprocess by replaying history to recompute results

This approach favors easier operations but relies on strong, idempotent stream processing and predictable event-time handling.

Choosing between Lambda and Kappa

  • Real-time analytics with simple corrective needs: Kappa is attractive when you can rely on a robust streaming stack.
  • Mixed workloads with heavy batch analytics and strict correctness: Lambda offers separation, at the cost of more infrastructure.
  • Data quality concerns or late-arriving data: Lambda helps by re-running batch computations, while Kappa may require careful reprocessing logic.

Example teams often start with Kappa for real-time dashboards. If later, long-running batch analytics or complex reconciliations become essential, they may add a Lambda-style batch path.

Key Takeaways

  • Lambda and Kappa are two patterns to build scalable data pipelines for large and fast data.
  • Lambda adds a batch layer for accuracy but increases complexity; Kappa uses one streaming path for simplicity.
  • The right choice depends on workload mix, latency goals, and how you handle late or corrected data.