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An enterprise is designing a cost-effective data lakehouse architecture on Google Cloud. Several petabytes of structured analytical data are maintained in open columnar formats (such as Parquet) across Cloud Storage buckets and external cloud object stores. The security team requires that analysts query the data using standard BigQuery SQL and authorized views with fine-grained access controls, while strictly prohibiting granting end users direct object read permissions (storage.objects.get) to the underlying storage buckets.
Which architecture should you implement to meet these requirements?
BigLake external tables extend BigQuery's compute and governance capabilities to data stored directly in object storage like Cloud Storage, Amazon S3, and Azure Blob Storage. They unify data lakes and warehouses by allowing analytical SQL queries on open file formats without requiring data duplication or ingestion into native storage.
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StudyCertsstorage.objects.get) on the Cloud Storage bucket.This approach satisfies the enterprise's governance requirement to prevent direct bucket access while delivering high query flexibility, open-format support, and centralized security administration.
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