PDE Designing Data Processing Systems Practice Question
An organization is implementing a data lake on Google Cloud using Cloud Storage. They need to process both batch and streaming data with a unified pipeline. The team has experience with Apache Beam. Which architecture should they use to minimize operational overhead?
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 with Cloud Dataflow using the same pipeline for batch and streaming
Kappa architecture uses a single streaming pipeline for both batch and streaming, simplifying operations. Dataflow implements Beam and supports both modes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Kappa architecture with Cloud Dataflow using the same pipeline for batch and streaming
Why this is correct
Cloud Dataflow runs the same Apache Beam pipeline for both bounded and unbounded sources, so one codebase serves batch and streaming. This satisfies the unified-pipeline requirement while remaining fully managed, minimising operational overhead for the Beam-experienced team.
- ✗
Use Cloud Dataproc for batch and Cloud Dataflow for streaming
Why it's wrong here
Cloud Dataproc runs Hadoop and Spark clusters requiring provisioning, scaling and patching, adding operational overhead, and it does not share a pipeline with Dataflow. Dataproc suits migrating existing Spark or Hadoop workloads, but here the team's Apache Beam experience and the unified-pipeline requirement point to Dataflow alone.
- ✗
Lambda architecture with Cloud Dataflow for batch and Cloud Pub/Sub for streaming
Why it's wrong here
Lambda architecture maintains two separate processing paths — a batch layer and a speed layer — plus a serving layer to merge them, duplicating logic and operational burden. It is tempting because it handles batch and streaming, but Apache Beam's unified model already processes both with one pipeline, which is the stated goal.
- ✗
Use Cloud Data Fusion for both batch and streaming
Why it's wrong here
Data Fusion is mainly batch; streaming support is limited.
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