Forum Discussion

ChiruGalla's avatar
ChiruGalla
Icon for Helper I rankHelper I
2 months ago
Solved

Microsof Fabric #Business Continuity & Disaster Recovery (BCDR) Specifications

For any business to remain successful and operational at all times, every organization must give Business Continuity and Disaster Recovery (BC/DR) planning the highest priority for all its applications — starting from the design phase, not as an afterthought.

Microsoft Fabric is a SaaS platform, and Microsoft manages the underlying infrastructure resilience — but the responsibility for an application-level BC/DR strategy still rests with the organization. A comprehensive, end-to-end BC/DR plan must be documented and validated through testing, covering workspace and capacity failover, data replication/backup strategy (e.g., OneLake redundancy, cross-region considerations), and RTO/RPO targets, along with recovery runbooks.

For reference, I've also outlined, at a high level, the key components that should be considered as part of the overall BC/DR strategy.

 

1. Governance & BCDR Architecture Framework:

The implementation vendor must design, document, and implement an automated Redeploy + Rehydrate cross-region BCDR architecture (Primary: Azure Region / Secondary: Azure Region).

RTO/RPO Compliance Matrix: Map all data pipelines, Lakehouses, and semantic models into Tier 1 (Critical), Tier 2 (Operational), and Tier

3 (Historical) recovery tiers.
Standard Operating Runbook: Deliver an end-to-end, step-by-step DR Execution Runbook detailing automated failover, manual failover(planned, forced failover), manual validation steps, role assignments, and failback procedures.
Conduct at least one fully simulated regional failover drill.

2. Technical Component Requirements Checklist:

A. Fabric Capacity & Provisioning

Infrastructure-as-Code (IaC): All Fabric Capacity configurations, capacity settings, tenant-level settings, and environment parameters must be codified using Terraform.
Provisioning of metadata SQL instances along with the right security and access mechanisms
Automated Scaling Scripting: Implement Azure CLI/PowerShell scripts to automatically scale up or unpause the secondary standby Fabric Capacity during a DR event.

B. Networking, Security & Gateways:

On-Premises Data Gateways (OPDG): Configure multi-node Gateway High Availability (HA) clusters across physically separate hosts to prevent single-point-of-failure network bottlenecks.
VNet Data Gateways & Private Endpoints: Route all Fabric traffic through Azure VNet Gateways tied to dual ExpressRoute/VPN connections with Private Link endpoints configured for both primary and secondary paired regions.
Entra ID & Security Preservation: Ensure all Service Principals, Managed Identities, Conditional Access policies, and Row-Level/Column-Level Security (RLS/CLS) rules are environment-agnostic and synced across regions.

C. OneLake Storage & Medallion Architecture:

Geo-Replication: Enable and test OneLake Geo-Replication at the Capacity level to ensure Delta Parquet tables (Bronze, Silver, Gold) asynchronously mirror to the secondary region
Ransomware & Corruption Protection: Implement Lakehouse Time Travel and VACUUM policies (minimum 7-day retention) paired with soft-delete capabilities.

D. Ingestion, Pipelines & Code Management (Git Integration):

100% Source Control Binding: All Fabric artifacts—including Data Pipelines, Dataflows Gen2, Spark Notebooks, Lakehouse definitions, and Warehouse schemas—must be integrated with Azure DevOps or GitHub.
Automated CI/CD Deployment: Deliver an automated Azure DevOps pipeline capable of rehydrating and deploying all workspace items into the secondary Fabric Capacity from source control without manual code copy-pasting.
E. Semantic Models & Power BI Presentation Layer:

Direct Lake Optimization: Structure semantic models in Direct Lake mode where applicable, ensuring connection paths dynamically point to secondary OneLake endpoints during failover.
Replication of Semantic model definitions

Automated Connection Repointing: Provide automated deployment rules or Fabric REST API scripts to repoint Power BI report datasets and connection strings to the failover region upon disaster declaration.

Unassisted Failover Demonstration: Execute a simulated Azure regional outage where workspaces and pipelines are successfully deployed into the secondary region using Git pipelines within the agreed RTO, per business.

Data Consistency Audit: Prove zero data loss or data corruption across the Medallion architecture within the agreed RPO, per business.

Have complete technical documentation, architecture diagrams, and train internal staff on executing the DR Runbook that should address both Planned and unplanned failover/failback mechanisms.

  • Hi ChiruGalla​,

    This is a useful starting checklist. One distinction I would make is between OneLake data replication and full workload recovery.

    When disaster recovery is enabled for a Fabric capacity, eligible Lakehouse and Warehouse data in OneLake is replicated asynchronously to an Azure-paired region. However, I would not treat that as automatic end-to-end failover for the entire Fabric solution. Microsoft’s Fabric reliability guidance explains that recovery requirements vary by Fabric experience, and data stored outside OneLake may require a separate strategy.

    For my own BCDR plan, I would validate each layer separately:

    • OneLake data replication and expected RPO
    • Capacity availability in the recovery region
    • Workspace and item redeployment through Git or deployment automation
    • Connections, credentials, gateways and permissions
    • Semantic-model and report rebinding
    • External source-system recovery
    • Post-recovery data and functional validation

    I would also avoid assuming that the secondary region or failover process is fully customer-controlled. Microsoft selects the paired region, and regional recovery can still require creating capacity, redeploying Fabric items and rehydrating workloads from the replicated data. The current OneLake disaster-recovery guidance provides the detailed limitations and recovery responsibilities.

    The most important part for me would be testing the runbook against every Fabric item type in scope, because the recovery process is not identical across Lakehouse, Warehouse, Power BI, Real-Time Intelligence and Data Factory workloads.

    Thank you for sharing the checklist! It raises several areas that every production Fabric implementation should evaluate.

    AI-assisted drafting: AI was used to help structure and phrase this response. I reviewed and validated the technical content before posting.

3 Replies

  • ShivekMaharaj's avatar
    ShivekMaharaj
    Icon for Community Champion rankCommunity Champion

    Hi ChiruGalla​,

    This is a useful starting checklist. One distinction I would make is between OneLake data replication and full workload recovery.

    When disaster recovery is enabled for a Fabric capacity, eligible Lakehouse and Warehouse data in OneLake is replicated asynchronously to an Azure-paired region. However, I would not treat that as automatic end-to-end failover for the entire Fabric solution. Microsoft’s Fabric reliability guidance explains that recovery requirements vary by Fabric experience, and data stored outside OneLake may require a separate strategy.

    For my own BCDR plan, I would validate each layer separately:

    • OneLake data replication and expected RPO
    • Capacity availability in the recovery region
    • Workspace and item redeployment through Git or deployment automation
    • Connections, credentials, gateways and permissions
    • Semantic-model and report rebinding
    • External source-system recovery
    • Post-recovery data and functional validation

    I would also avoid assuming that the secondary region or failover process is fully customer-controlled. Microsoft selects the paired region, and regional recovery can still require creating capacity, redeploying Fabric items and rehydrating workloads from the replicated data. The current OneLake disaster-recovery guidance provides the detailed limitations and recovery responsibilities.

    The most important part for me would be testing the runbook against every Fabric item type in scope, because the recovery process is not identical across Lakehouse, Warehouse, Power BI, Real-Time Intelligence and Data Factory workloads.

    Thank you for sharing the checklist! It raises several areas that every production Fabric implementation should evaluate.

    AI-assisted drafting: AI was used to help structure and phrase this response. I reviewed and validated the technical content before posting.

  • v-achippa's avatar
    v-achippa
    Icon for Community Support rankCommunity Support

    Hi ChiruGalla,

     

    Thank you for reaching out to Microsoft Fabric Community.

     

    Thank you ShivekMaharaj for the prompt response.

     

    As we haven’t heard back from you, we wanted to kindly follow up to check if the solution provided by the user for the issue worked?  or let us know if you need any further assistance.

     

    Thanks and regards,

    Anjan Kumar Chippa

    • v-achippa's avatar
      v-achippa
      Icon for Community Support rankCommunity Support

      Hi @ChiruGalla,

       

      We wanted to kindly follow up to check if the solution provided by the user for the issue worked?  or let us know if you need any further assistance.

       

      Thanks and regards,

      Anjan Kumar Chippa