Lambda vs Kappa Architecture AI Prompt

Evaluate whether this use case calls for a Lambda architecture or a Kappa architecture.

Use case: {{use_case_description}}
Latency requirements: {{latency}}
Historical reprocessing need: {{reprocessing_need}}
Team size and complexity tolerance: {{team_constraints}}

1. Lambda architecture:
   - Two separate pipelines: batch (accurate, slow) and speed (fast, approximate)
   - Serving layer merges batch and speed views
   - Pros: handles historical reprocessing naturally, speed layer can be simpler
   - Cons: two codebases for the same logic (duplication and drift risk), higher operational complexity
   - When to choose: if batch and streaming have genuinely different business logic, or if batch accuracy is non-negotiable and streaming is additive

2. Kappa architecture:
   - Single streaming pipeline for everything
   - Reprocessing = replay from beginning of the message log with a new consumer group
   - Pros: single codebase, simpler operations, no view merging
   - Cons: requires a long-retention message log, streaming system must handle batch-scale replay, stateful processing is more complex
   - When to choose: when batch and streaming logic are identical, team wants to minimize operational surface area

3. Decision framework:
   - Is the processing logic identical for batch and streaming? → Kappa
   - Do you need to reprocess years of history frequently? → Check if Kappa replay is cost-effective
   - Is your team small? → Kappa (less to maintain)
   - Do you have complex, different historical vs real-time logic? → Lambda
   - Is latency requirement < 1 minute AND accuracy is critical? → Lambda with micro-batch

4. Recommended architecture for this use case:
   - State the recommendation clearly with rationale
   - Identify the top 2 risks of the chosen approach and mitigations

Return: architecture comparison, decision framework applied to this use case, recommendation, and risk register.