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A financial analytics company ingests terabytes of transaction logs daily into Google Cloud. Analysts frequently query recent log data within a 30-day window, but analytical queries occasionally analyze records up to 90 days old. Logs older than 90 days are no longer queried in the data warehouse and must automatically expire to control operational expenditure. Additionally, raw audit files stored in Cloud Storage must be preserved for three years for compliance, transitioning automatically to lower-cost storage tiers as access frequency declines.
Which storage and lifecycle design should the cloud architect implement to balance query performance, data availability, and cost?
This architecture combines BigQuery partition-level expiration with Cloud Storage Object Lifecycle Management (OLM) to automate data retention and tiered storage across analytics and object storage systems.
StudyCerts
StudyCertspartition_expiration_days = 90 ensures that individual partition slices automatically expire and are deleted once they reach 90 days, eliminating unnecessary data warehouse storage costs without requiring scheduled deletion scripts.Native BigQuery partition expiration combined with Cloud Storage Object Lifecycle Management provides a fully managed, serverless lifecycle tiering framework that requires zero custom operational compute infrastructure.
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