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: Catastrophic Regional Outage: Must achieve a low Recovery Time Objective (RTO) and Recovery Point Objective (RPO) if an entire Google Cloud region experiences physical destruction or extended downtime. Logical Data Corruption / Accidental Deletion: Must be able to recover tables corrupted or dropped by faulty ETL jobs that are discovered up to 12 days after the incident. Data Locality: All primary and secondary data must remain within defined national geographic boundaries (for example, within the United States). Which storage and operational architecture correctly satisfies these business and recovery requirements?

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.: ### What the Solution Is 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. ### How It Meets Regional Disaster Requirements * **Protection against physical regional loss:** BigQuery multi-region configurations (such as the `US` 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. * **Low RTO and RPO:** Replicating datasets across regions and establishing a **failover reservation** with pre-provisioned slots in the secondary region ensures minimal data lag and rapid failover execution during a regional disaster. ### How It Meets Corruption and Recovery SLAs * **Time travel window:** By default, BigQuery retains modified or deleted storage blocks for up to **7 days**, allowing self-service point-in-time SQL queries and snapshot table restorations. * **Fail-safe period:** After the 7-day time travel window ends, BigQuery automatically retains deleted data for an additional **7 days** (covering up to 14 total days from the modification/deletion event). For corruptions discovered at day 12, data can be restored through **Cloud Customer Care**. ### Key Advantages * **Guarantees regional redundancy** without shared fate or overlapping failure domains. * **Combines automated replication** with compute failover readiness. * **Protects historical states** across a total 14-day retention buffer without requiring manual daily full-table exports. ### Why This Is Optimal 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.

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Bigquery Ha Dr Sla Architecture Design

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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:

Catastrophic Regional Outage: Must achieve a low Recovery Time Objective (RTO) and Recovery Point Objective (RPO) if an entire Google Cloud region experiences physical destruction or extended downtime.
Logical Data Corruption / Accidental Deletion: Must be able to recover tables corrupted or dropped by faulty ETL jobs that are discovered up to 12 days after the incident.
Data Locality: All primary and secondary data must remain within defined national geographic boundaries (for example, within the United States).

Which storage and operational architecture correctly satisfies these business and recovery requirements?

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.

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.

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.

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.

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Bigquery Ha Dr Sla Architecture Design

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