After a metric is tracked by Databand, you can compare the metric across all run histories from the relevant pipeline, along with other values from that run. Databand will keep all metrics in the context of the pipeline and run where the metric was produced, making it easier for users to correlate metadata and trace the root cause of issues. For example, you can quickly identify how pipeline performance or application errors from a specific run relate to changes in data volumes or resource consumption levels.
You can analyze run metrics from a pipeline level view (as shown in the screen above) or from the perspective of any given metric, and see how it trends over previous run values, alone or correlated with other metric values. This is especially practical if you have associated metrics that together have a big impact on pipeline reliability, such as CPU utilization, memory utilization, and data volumes with run durations.
Updated about 1 month ago