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Backup, DR & Cross-Region Replication

GCP Data EngineeringBackup & Recovery⭐ Premium

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Backup, DR & Cross-Region Replication

Master backup and disaster recovery on GCP including cross-region replication, BigQuery backups, GCS replication, and DR strategies.

14 min readIntermediate

DR Architecture

πŸ—οΈ GCP Data Engineering Reference Architecture
DATA SOURCESπŸ—ƒοΈOn-Prem DB☁️SaaS APIsπŸ“‘IoT SensorsπŸ“±Mobile AppsπŸ”ŒREST APIsINGESTION LAYERDataflow (CDC)Pub/SubCloud TasksStorage TransferTransfer ApplianceRAW DATA ZONE (Cloud Storage)landing/Ingested databronze/Unvalidatedarchive/Historicalraw/Original formatstaging/Temp processingPROCESSING LAYERDataflowStream + BatchDataprocSpark/HadoopCloud FunctionsEvent-drivenData PrepVisual ETLCloud ComposerOrchestrateCURATED DATA ZONEsilver/Cleaned, validatedgold/Business-readyaggregates/Pre-computedfeatures/ML featuresBigQuery (Warehouse)Looker (BI)Vertex AI (ML)Data StudioDataplex
Interview Tip: GCP's data engineering stack is serverless-first. Dataflow (Apache Beam) handles both streaming and batch. BigQuery is the flagship analytics service.

GCS Cross-Region Replication

from google.cloud import storage

client = storage.Client()

# Create dual-region bucket (automatic replication)
bucket = client.bucket("my-critical-data")
bucket.storage_class = "STANDARD"
bucket.location = "US-EAST1"  # Pairs with US-EAST4
bucket.location_type = "dual-region"

bucket = client.create_bucket(bucket, exists_ok=True)
print(f"Created dual-region bucket: {bucket.name}")

BigQuery Cross-Region Backup

from google.cloud import bigquery

client = bigquery.Client()

# Copy dataset to another region for DR
job = client.copy_table(
    "my-project:analytics.sales",
    "my-project:dr_backup.sales_backup",
    job_config=bigquery.CopyJobConfig(
        write_disposition=bigquery.WriteDisposition.WRITE_TRUNCATE,
        destination_encryption_configuration=None
    )
)

job.result()
print(f"Backup completed: {job.output_rows} rows copied")

✨

Best Practice: Use dual-region GCS buckets for critical data (11 9s availability). Schedule BigQuery dataset copies for DR. Implement automated backup verification. Test DR procedures quarterly. Document RPO/RTO requirements for each data tier.

πŸ’¬

Common Interview Questions

Q1: What is the difference between RPO and RTO?

Answer: RPO (Recovery Point Objective) is the maximum acceptable data loss measured in time. RTO (Recovery Time Objective) is the maximum acceptable downtime. RPO determines backup frequency; RTO determines infrastructure requirements.

Q2: When would you use dual-region vs. multi-region GCS?

Answer: Dual-region provides 11 9s availability with lower latency between two specific regions. Multi-region provides 4 9s availability across all regions in a geographic area. Use dual-region for critical data requiring high availability and specific data residency.

Q3: How do you test disaster recovery procedures?

Answer: 1) Regular DR drills (quarterly), 2) Restore from backups to test environment, 3) Validate data integrity, 4) Measure actual RPO/RTO, 5) Document and update procedures based on findings.

Q4: What are the DR options for BigQuery?

Answer: 1) Cross-region dataset copies, 2) Time-travel (7 days), 3) Table snapshots, 4) Export to GCS, 5) BigQuery Omni for multi-cloud. Combine these strategies based on RPO/RTO requirements.

Q5: How do you handle DR for streaming pipelines?

Answer: 1) Use Pub/Sub multi-region topics, 2) Deploy Dataflow jobs in multiple regions, 3) Use Cloud Composer environment in secondary region, 4) Implement failover automation, 5) Test failover regularly.

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