An enterprise runs mission-critical analytical workflows on Cloud Composer in the us-central1 region. The data engineering team must implement an automated cross-region disaster recovery (DR) strategy to fail over orchestration to us-east4 in the event of an outage. The solution must satisfy the following technical requirements: Maintain configuration and version parity across the primary and failover environments. Continuously validate and monitor automated snapshot creation, alerting on any failures. Orchestrate automated snapshot loading, environment configuration updates, and execution handoff during a failover event. Prevent inconsistent state and side effects caused by re-executing tasks that ran after the latest snapshot. Which architecture and recovery workflow should the team deploy?

Maintain synchronized Terraform definitions to provision identical Composer and Airflow versions in both regions. Store scheduled primary snapshots in a multi-region Cloud Storage bucket, monitor snapshot health using Cloud Monitoring alerting on the snapshot creation metric, deploy a Cloud Function triggered by disaster alerts to pause primary DAGs, load the latest snapshot into the failover environment, and ensure all DAG tasks are designed to be idempotent.: ### What this solution is This solution establishes a resilient **cross-region disaster recovery** architecture for **Cloud Composer** workflows using **environment snapshots**, **Terraform infrastructure as code (IaC)**, **Cloud Monitoring alerting**, and **Cloud Functions** for automated failover orchestration. ### How it meets all requirements - **Configuration and version parity**: Maintaining synchronized **Terraform definitions** ensures that both primary (`us-central1`) and secondary (`us-east4`) environments share identical **Cloud Composer versions**, **Airflow builds**, environment sizes, and PyPI dependencies. - **Snapshot validation and monitoring**: Scheduled snapshots are written to a **multi-region Cloud Storage bucket** outside the primary failure domain. Creating a **Cloud Monitoring alert policy** on the `Snapshot creation count` metric filtered by `result = SUCCEEDED` guarantees proactive alerting if automated snapshot capture fails. - **Automated failover workflow**: A **Cloud Function** triggered by monitoring alert notifications automates the failover sequence by issuing API calls to pause DAGs in the primary environment, loading the latest healthy snapshot into the `us-east4` failover environment, and applying regional connection overrides. - **Idempotent task execution**: Because environment snapshots represent a discrete point-in-time state, Airflow schedules and replays tasks that may have already executed after snapshot capture. Designing all DAG tasks to be **idempotent** ensures that duplicate executions do not corrupt downstream data. ### Key advantages - **Automated recovery execution**: Eliminates manual intervention delays during regional disasters by triggering programmatic snapshot restoration. - **Resilient snapshot storage**: Multi-region bucket placement ensures snapshot availability even if an entire Google Cloud region experiences downtime. - **State preservation**: Restores Airflow database metadata, DAG run histories, variables, and connection configurations seamlessly into the failover cluster. ### Why this is optimal This approach aligns with Google Cloud site reliability engineering best practices by combining immutable infrastructure definitions, automated monitoring triggers, decoupled multi-region backups, and idempotent workflow design.

Cloud Composer Cross Region Dr Snapshots | professional-cloud-data-engineer

Cloud Composer Cross Region Dr Snapshots

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An enterprise runs mission-critical analytical workflows on Cloud Composer in the us-central1 region. The data engineering team must implement an automated cross-region disaster recovery (DR) strategy to fail over orchestration to us-east4 in the event of an outage.

The solution must satisfy the following technical requirements:

Maintain configuration and version parity across the primary and failover environments.
Continuously validate and monitor automated snapshot creation, alerting on any failures.
Orchestrate automated snapshot loading, environment configuration updates, and execution handoff during a failover event.
Prevent inconsistent state and side effects caused by re-executing tasks that ran after the latest snapshot.

Which architecture and recovery workflow should the team deploy?

Configure Cloud Composer in High Resilience mode across two zones in us-central1, stream Airflow metadata logs to Cloud Logging, and rely on Cloud SQL automatic cross-zone failover without snapshot restoration or separate secondary environments.

Store environment snapshots in the primary environment's default Cloud Storage bucket, configure Cloud Scheduler to trigger hourly snapshot export jobs via gcloud CLI, and execute a Terraform script during failover that upgrades the secondary environment to the latest Composer version.

Maintain synchronized Terraform definitions to provision identical Composer and Airflow versions in both regions. Store scheduled primary snapshots in a multi-region Cloud Storage bucket, monitor snapshot health using Cloud Monitoring alerting on the snapshot creation metric, deploy a Cloud Function triggered by disaster alerts to pause primary DAGs, load the latest snapshot into the failover environment, and ensure all DAG tasks are designed to be idempotent.

Enable asynchronous Persistent Disk replication between the primary and secondary Cloud Composer GKE worker nodes, configure Cloud Monitoring to detect node health degradation, and script Cloud Functions to force-attach the replicated disks to a new GKE cluster in us-east4.

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Cloud Composer Cross Region Dr Snapshots

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