PDE Maintaining and Automating Data Workloads Practice Question
A company runs a Dataflow streaming pipeline that processes financial transactions. They need to apply a new transformation that enriches the data with a lookup from Cloud Bigtable without stopping the pipeline. The pipeline must be updated in a way that minimises data loss and preserves exactly-once semantics. What is the recommended approach?
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
✓
Use the Dataflow update option with the same pipeline name and new version, ensuring the transform is backward compatible.
Dataflow supports updating a streaming pipeline without draining by replacing the pipeline version. Using the --update flag with the same pipeline name and a new version allows the pipeline to be upgraded while preserving the state exactly-once.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use the Dataflow update option with the same pipeline name and new version, ensuring the transform is backward compatible.
Why this is correct
Updating preserves state and exactly-once semantics.
- ✗
Drain the pipeline first, then start a new pipeline with the updated code.
Why it's wrong here
Draining stops the pipeline and may lose some data if not done properly.
- ✗
Create a new pipeline in parallel and switch the Pub/Sub subscription to the new pipeline.
Why it's wrong here
Switching subscriptions may cause duplicate processing; updating is the recommended method.
- ✗
Stop the pipeline, update the code, and restart with a new pipeline name.
Why it's wrong here
This would lose in-flight data and violate exactly-once.
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