Overview
Lambda Architecture and Kappa Architecture are two patterns for building big-data pipelines that need both real-time and historical results. Lambda runs parallel batch and speed layers that get merged at query time, while Kappa pushes everything through a single, replayable stream pipeline. The choice determines how much duplicate logic you maintain and how reprocessing actually works.
Comparison Diagram
Comparison Table
| Aspect | Lambda Architecture | Kappa Architecture |
|---|---|---|
| Ingestion path | Raw events are forked to both a batch store and a stream processor at once | Raw events are written once to an immutable, replayable log (e.g. Kafka) |
| Processing model | Two independent codebases — a batch job and a stream job — implement the same logic twice | One stream-processing codebase handles both real-time and historical computation |
| Historical reprocessing | The batch layer periodically recomputes results over the entire raw dataset | Reprocessing means replaying the log from an earlier offset through the same stream job |
| State and storage | Separate batch views and speed views are maintained independently, often in different stores | A single serving store is continuously updated by the stream processor |
| Result merging | The query layer merges or reconciles batch and speed views at read time | No merge step — the stream processor’s output is the only view |
| Consistency behavior | Speed layer results are approximate until the batch layer overwrites them, so the two can disagree temporarily | One computation path avoids batch/speed drift, but correctness depends entirely on the stream engine’s guarantees |
| Operational overhead | Higher — two parallel pipelines to build, deploy, and monitor, with duplicated logic | Lower pipeline count, but requires a log system with long enough retention to support full replays |
| Best-fit scenario | Batch and speed logic genuinely differ, or the org already has mature batch infrastructure | Team wants one canonical pipeline and has a stream engine that can absorb both live and replay traffic |
Key Differences
- Lambda splits ingestion across a batch layer and a speed layer, while Kappa sends everything through one stream.
- Reprocessing history in Lambda means rerunning a full batch job; in Kappa it means replaying the log through the same stream code.
- Lambda’s query layer must reconcile two separate views; Kappa exposes a single serving store with no merge step.
- Kappa’s design hinges on a durable, long-retention event log capable of full replays, which Lambda does not require.
When to Use Each
Lambda Architecture
- Divergent batch/speed logic: Use Lambda when the approximate real-time algorithm genuinely differs from the exact batch algorithm, such as retrained offline models versus simple real-time aggregates.
- Existing batch infrastructure: Organizations with mature Hadoop or Spark batch pipelines can bolt on a speed layer without migrating everything to streaming.
- Infrequent, cheap recompute: When periodic full recomputation is inexpensive and rare, the overhead of maintaining two pipelines is easier to absorb.
Kappa Architecture
- Single source of truth: Use Kappa when eliminating batch/speed drift and reconciliation logic is a priority for correctness or simplicity.
- Kafka-centric stack: Teams already running a durable, replayable log with sufficient retention can reprocess history without a separate batch system.
- Frequent logic changes: Deploying updated business logic is simpler with one pipeline than coordinating synchronized changes across batch and speed code.