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Designing Data Processing SystemsmediumMultiple ChoiceObjective-mapped

PDE Designing Data Processing Systems Practice Question

A company wants to design a data pipeline for real-time fraud detection. The system must process streaming financial transactions, enrich them with user profiles from a lookup table, and flag suspicious activities within seconds. Which architecture pattern would be MOST suitable?

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

Kappa architecture using a single stream processing framework like Apache Beam

Kappa architecture uses a single stream processing engine to handle both real-time and batch reprocessing, simplifying the pipeline. Lambda architecture requires maintaining separate batch and streaming layers, increasing complexity. The scenario only requires real-time processing with enrichment, so Kappa is more appropriate.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Pub/Sub combined with Cloud Functions for stateless processing

    Why it's wrong here

    Cloud Functions are stateless and not designed for stream processing with stateful operations like enrichment and windowing.

  • Kappa architecture using a single stream processing framework like Apache Beam

    Why this is correct

    Kappa processes everything as a stream, suitable for real-time fraud detection with enrichment from a side input.

  • Batch processing with hourly micro-batches using Dataflow

    Why it's wrong here

    Hourly batches would be too slow for real-time fraud detection.

  • Lambda architecture with a batch layer for historical analysis and a speed layer for real-time processing

    Why it's wrong here

    Lambda adds unnecessary complexity. The need is purely real-time fraud detection, not batch historical analysis.

Quick reference

Cloud Service Model Comparison

ModelYou ManageProvider ManagesExamples
IaaSOS, runtime, apps, dataHardware, hypervisor, networkingEC2, Azure VMs, GCP Compute Engine
PaaSApps and dataOS, runtime, middleware, hardwareElastic Beanstalk, Azure App Service
SaaSData and settings onlyEverything elseMicrosoft 365, Salesforce, Workday
FaaS / ServerlessFunction code onlyInfra, scaling, runtimeLambda, Azure Functions, Cloud Run
CaaSContainers and appsKubernetes, OS, hardwareEKS, AKS, GKE

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