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A data engineering team operates an enterprise data pipeline on Google Cloud that ingests transactional records into analytical storage. During recent pipeline runs, upstream schema mismatches and partial extraction failures caused silent data corruption and missing records, which went undetected until downstream reports failed.
The team needs to establish a proactive observability and alerting strategy to rapidly detect data completeness, freshness, and consistency anomalies. The strategy must:
Which strategy should the team implement?
Configure a Cloud Logging sink to export all execution logs to BigQuery and schedule hourly BigQuery queries that inspect data anomalies and send notifications.
Rely on predefined Google Cloud service dashboards in Cloud Monitoring for the underlying storage resources and configure default synthetic uptime checks to monitor data completeness.
Emit custom telemetry and log-based metrics to Cloud Monitoring for record completeness and processing freshness, configure metric-threshold alerting policies linked to notification channels, and build custom Cloud Monitoring dashboards that combine metric charts, log data widgets, and incident status panels.
Configure the Ops Agent on underlying Compute Engine virtual machines to collect host-level metrics and configure alerts exclusively on VM memory and CPU utilization thresholds.
Configure a Cloud Logging sink to export all execution logs to BigQuery and schedule hourly BigQuery queries that inspect data anomalies and send notifications.
Rely on predefined Google Cloud service dashboards in Cloud Monitoring for the underlying storage resources and configure default synthetic uptime checks to monitor data completeness.
Emit custom telemetry and log-based metrics to Cloud Monitoring for record completeness and processing freshness, configure metric-threshold alerting policies linked to notification channels, and build custom Cloud Monitoring dashboards that combine metric charts, log data widgets, and incident status panels.
Google Cloud Monitoring and Cloud Logging provide an integrated observability framework that allows data teams to capture, visualize, and alert on pipeline health and data quality metrics. By emitting custom user-defined metrics or extracting log-based metrics from application logs, pipelines can track business-level data characteristics such as record counts, validation error rates, and end-to-end processing latencies.
This architecture directly leverages native Google Cloud Observability capabilities without introducing unnecessary architectural overhead, ensuring low latency between error occurrence and incident alerting.
Configure the Ops Agent on underlying Compute Engine virtual machines to collect host-level metrics and configure alerts exclusively on VM memory and CPU utilization thresholds.