Your organization has an aggregated log sink that routes all Admin Activity logs from every project into a regional log bucket named "fin-logs" in the project "audit-central" (region: us-central1). Security engineers now want to run ad-hoc SQL queries against these logs directly in Cloud Logging without incurring the additional storage costs of exporting them to BigQuery. Which single configuration change will provide this capability?
Enable Log Analytics on the existing "fin-logs" bucket so it can be queried with SQL in place.
Create a BigQuery sink that exports the Admin Activity logs from "fin-logs" to a regional BigQuery dataset.
Enable the Cloud Logging API for the "audit-central" project to unlock SQL querying features.
Provision a second log bucket with Log Analytics enabled and reconfigure the aggregated sink to route logs there.
Log Analytics lets you execute BigQuery-syntax SQL directly on data that remains in a Cloud Logging bucket. To use it you simply upgrade an existing user-managed bucket (any bucket other than _Required) by enabling the Log Analytics feature; no export or new sink is required, and storage costs do not change because the logs stay in the same bucket. Exporting to BigQuery, creating a new analytics-enabled bucket, or merely enabling the Cloud Logging API would not satisfy the requirement as they either increase cost, add unnecessary complexity, or still leave the logs unqueryable with SQL inside Cloud Logging.
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What is Log Analytics in GCP Cloud Logging?
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How does enabling Log Analytics differ from exporting logs to BigQuery?
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Why can't merely enabling the Cloud Logging API provide SQL querying capabilities?
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What are Admin Activity Logs in GCP?
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What is Log Analytics in Cloud Logging?
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How does enabling Log Analytics save costs compared to exporting logs to BigQuery?
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