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A data analyst is monitoring a newly deployed Dataflow batch pipeline in the Google Cloud console to verify its health and check for potential bottlenecks.
While viewing the pipeline details, they need to inspect the status of individual processing stages (such as Running, Succeeded, or Failed) and evaluate step-level performance details.
Which feature in the Dataflow job UI should the analyst use to inspect individual stage statuses and their associated step metrics?
The Dataflow execution graph (also known as the job graph) is the visual representation of a pipeline's execution steps within the Google Cloud console. In Dataflow, Google Cloud optimizes Apache Beam pipelines by fusing compatible transforms into execution stages. The console visually represents these stages as connected nodes that show execution status in real time.
Interacting directly with the execution graph is the standard, native way to drill down into stage-level execution within Dataflow. It links high-level pipeline flow directly to granular stage metrics and filtered worker logs without requiring custom scripts or navigating away from the job dashboard.
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