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A financial enterprise is planning a disaster recovery (DR) and high-availability architecture for its mission-critical BigQuery analytics warehouse to meet stringent business SLAs:
Which storage and operational architecture correctly satisfies these business and recovery requirements?
Store the primary dataset in us-central1, replicate the dataset to the US multi-region using the BigQuery Data Transfer Service, and export tables to Coldline Cloud Storage every 14 days.
Deploy the dataset in us-central1 with a 7-day time travel window, and configure a Cloud Storage dual-region bucket with Turbo Replication to continuously stream BigQuery change data capture (CDC) logs.
Configure cross-region dataset replication between two distinct regions (such as us-central1 and us-east1) with a failover reservation, and rely on BigQuery's 7-day time travel plus 7-day fail-safe window for recovering older corruptions via Cloud Customer Care.
Create the primary dataset in the US multi-region location, configure the time travel window to 14 days at the dataset level, and rely on automatic multi-region failover.
Store the primary dataset in us-central1, replicate the dataset to the US multi-region using the BigQuery Data Transfer Service, and export tables to Coldline Cloud Storage every 14 days.
Deploy the dataset in us-central1 with a 7-day time travel window, and configure a Cloud Storage dual-region bucket with Turbo Replication to continuously stream BigQuery change data capture (CDC) logs.
Configure cross-region dataset replication between two distinct regions (such as us-central1 and us-east1) with a failover reservation, and rely on BigQuery's 7-day time travel plus 7-day fail-safe window for recovering older corruptions via Cloud Customer Care.
This architecture pairs cross-region dataset replication and failover reservations across two distinct single regions (us-central1 and us-east1) with BigQuery's built-in time travel and fail-safe retention mechanisms to achieve comprehensive disaster recovery.
StudyCerts
StudyCertsUS multi-region) store data within a single region in that geography and do not provide automated regional redundancy. Pairing two distinct regions like us-central1 and us-east1 avoids shared failure domains while strictly complying with US data residency.This design directly maps the operational SLAs to native Google Cloud features: cross-region replication handles physical infrastructure disasters, while the combined 14-day time travel and fail-safe retention covers long-tail logical corruption.
Create the primary dataset in the US multi-region location, configure the time travel window to 14 days at the dataset level, and rely on automatic multi-region failover.