An enterprise is modernizing a distributed e-commerce application running across multiple Google Cloud projects. The operations and compliance teams have established the following observability and data governance requirements: Capture structured application telemetry and error events in Cloud Logging without altering application runtime code. Continuously export security and compliance-related log entries to BigQuery for SQL-based long-term audit analysis. Track the frequency of specific application-level transaction failure codes that appear inside log payloads as a metric in Cloud Monitoring dashboards and alerts. Which architecture should the cloud architect recommend to satisfy these requirements?

Create a log sink in Cloud Logging with an export destination of BigQuery filtered on compliance events, and define a logs-based counter metric to extract transaction failure codes into Cloud Monitoring.: ### What the Solution Is **Cloud Logging sinks** and **logs-based metrics** are native Google Cloud Operations features designed to route log data efficiently and bridge log event data into **Cloud Monitoring** telemetry without requiring code modifications. ### How It Meets the Requirements - **Continuous Export to BigQuery**: A **log sink** uses a filter query to match security and compliance logs and routes them directly to a **BigQuery dataset** for long-term retention, schema management, and SQL analysis. - **Extracting Metrics from Log Payloads**: A **logs-based counter metric** scans incoming log entries matching specific filter criteria (e.g., specific transaction failure codes in JSON payloads) and increments a custom metric in **Cloud Monitoring** in real time. - **No Code Changes**: Both features operate on logs already ingested by Cloud Logging, avoiding custom telemetry pipelines or application-level refactoring. ### Key Advantages and Capabilities - **Native Platform Integration**: Fully managed, serverless log routing and metric extraction with near zero maintenance overhead. - **Real-Time Telemetry**: Logs-based metrics become immediately available in Cloud Monitoring for alerting policies, service-level objectives (SLOs), and dashboards. - **Cost and Retention Optimization**: Sinks allow selective routing so that only relevant compliance logs are stored in BigQuery, minimizing storage costs. - **Rich Filtering**: Supports advanced filter expressions matching structured `jsonPayload` fields and log labels. ### Why This Is Optimal This architecture achieves real-time metric tracking and regulatory data routing using fully managed, out-of-the-box Google Cloud Operations services, avoiding the architectural complexity, latency, and operational burden of custom streaming pipelines.

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Cloud Logging Sinks Logs Based Metrics

Section: Observability

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An enterprise is modernizing a distributed e-commerce application running across multiple Google Cloud projects. The operations and compliance teams have established the following observability and data governance requirements:

Capture structured application telemetry and error events in Cloud Logging without altering application runtime code.
Continuously export security and compliance-related log entries to BigQuery for SQL-based long-term audit analysis.
Track the frequency of specific application-level transaction failure codes that appear inside log payloads as a metric in Cloud Monitoring dashboards and alerts.

Which architecture should the cloud architect recommend to satisfy these requirements?

Configure an OpenTelemetry collector sidecar to bypass Cloud Logging, streaming audit records directly to BigQuery and sending custom metrics to Cloud Monitoring.

Stream all application logs to a Pub/Sub topic, process the stream with a Cloud Functions trigger to insert metrics via the Cloud Monitoring API, and write compliance records into BigQuery.

Enable Error Reporting notifications linked to a Cloud Monitoring alerting policy and retain logs exclusively within the default Cloud Logging bucket for compliance queries.

Create a log sink in Cloud Logging with an export destination of BigQuery filtered on compliance events, and define a logs-based counter metric to extract transaction failure codes into Cloud Monitoring.

Professional Cloud Architect

Cloud Logging Sinks Logs Based Metrics

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