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
Kappa architecture uses a single streaming pipeline; Dataflow can handle both by replaying data.
- ✗
Use Cloud Dataproc for batch and Cloud Dataflow for streaming
Why it's wrong here
This is not unified; two separate systems.
- ✗
Lambda architecture with Cloud Dataflow for batch and Cloud Pub/Sub for streaming
Why it's wrong here
Lambda architecture requires maintaining two separate codebases, increasing overhead.
- ✗
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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