Explanation:
The correct answer is C. Updating the pipeline using the --update option and setting the --jobName to the existing job name allows for a seamless transition to the new pipeline version. This method ensures that the Dataflow service performs a compatibility check to apply the update without disrupting ongoing data processing, thus preventing data loss. Options A and D involve stopping the pipeline, which can lead to downtime or data loss. Option B creates a new job instead of updating the existing one, which may result in data duplication or loss.
Ultimate access to all questions.
No comments yet.
What is the recommended method to update a running Cloud Dataflow pipeline to a new version without losing any data?
A
Stop the pipeline with the Drain option and create a new job with the updated code.
B
Update the pipeline using the --update option and set the --jobName to a new unique job name.
C
Update the pipeline using the --update option and set the --jobName to the existing job name.
D
Stop the pipeline with the Cancel option and create a new job with the updated code.