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A data engineer manages a nightly data processing pipeline that writes custom error messages into Cloud Logging whenever a data validation step fails. The team wants to track the frequency of these validation failures over time on a dashboard and trigger automated email notifications whenever the failure count exceeds a predefined threshold.
Which solution should the engineer implement to meet these requirements?
Export the pipeline logs to BigQuery using a log sink, and configure BigQuery Data Transfer Service to trigger alerting notifications when error counts rise.
Create a log-based metric in Cloud Logging to count occurrences of the error logs, then create an alerting policy and dashboard chart in Cloud Monitoring using this metric.
Install the Ops Agent on Compute Engine instances to collect VM CPU utilization metrics and configure synthetic monitors for threshold alerts.
Configure Cloud Monitoring uptime checks to poll the Cloud Logging API once per minute and trigger an incident when error records are found.
Export the pipeline logs to BigQuery using a log sink, and configure BigQuery Data Transfer Service to trigger alerting notifications when error counts rise.
Create a log-based metric in Cloud Logging to count occurrences of the error logs, then create an alerting policy and dashboard chart in Cloud Monitoring using this metric.
Log-based metrics are a feature of Cloud Logging that converts log data into quantifiable numeric time-series data. They allow you to define filters over incoming logs to extract numeric values or count how often specific text patterns or event types occur. Once created, these metrics seamlessly integrate into Cloud Monitoring as custom time series.
This approach uses built-in Google Cloud Observability capabilities to translate unstructured log entries into structured, actionable time-series metrics. It eliminates the need for custom scripts, separate database storage, or external polling mechanisms.
Install the Ops Agent on Compute Engine instances to collect VM CPU utilization metrics and configure synthetic monitors for threshold alerts.
Configure Cloud Monitoring uptime checks to poll the Cloud Logging API once per minute and trigger an incident when error records are found.
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