Cloud data warehouses remove many infrastructure tasks, but they do not remove the need for disaster recovery.
A managed platform may protect physical infrastructure while the organization remains responsible for logical data loss, bad transformations, deleted objects, compromised credentials, broken ingestion, configuration mistakes and the downstream BI systems that depend on the warehouse.
A practical recovery plan therefore starts with business services, not backup features.
Actiknow’s business intelligence services span data integration, modeling, dashboards, publishing and refresh mechanisms. That complete path is important because restoring warehouse tables is not enough if pipelines, semantic models and executive reports remain unavailable.
Define RPO and RTO by Business Process
Recovery Point Objective, or RPO, describes how much data loss the business can tolerate.
Recovery Time Objective, or RTO, describes how long the service can remain unavailable.
Do not define one pair for the entire warehouse.
Executive sales reporting may tolerate several hours of delay. Financial close may require a tighter recovery point. Operational analytics used throughout the day may have different expectations.
For each critical data product, record:
- business owner;
- maximum acceptable data loss;
- maximum acceptable outage;
- critical sources;
- critical transformations;
- downstream consumers;
- manual fallback if any.
This turns disaster recovery from an infrastructure exercise into a business continuity plan.
Identify Failure Scenarios
Plan for more than total platform outage.
Common scenarios include:
- accidental table deletion;
- incorrect overwrite or transformation;
- source connector failure;
- expired credentials;
- schema change;
- corrupted or duplicated load;
- warehouse object misconfiguration;
- security policy error;
- region or service disruption;
- deleted semantic model;
- broken gateway;
- BI capacity or service problem.
Each scenario can require a different response.
A platform-level recovery feature may help with deleted data but not with a credential that prevents pipelines from running.
Protect Data and Code Separately
Data and transformation logic have different recovery needs.
Warehouse data may rely on platform retention, snapshots, replication or other native capabilities.
Transformation code should be version-controlled where practical. Pipeline definitions, infrastructure configuration and deployment artifacts should also be reproducible.
If the team can restore yesterday’s data but cannot recreate the transformations that produced today’s curated models, recovery is incomplete.
Actiknow’s production-readiness guide emphasizes supportability and controlled production operation beyond a successful proof of concept. Recovery is one of the clearest tests of whether a data platform is truly operationalized.
Inventory Credentials and Secrets
Recovery often fails at the authentication layer.
Document which pipelines depend on service accounts, OAuth connections, API keys, certificates, gateways and network rules.
Store secrets through appropriate secret-management mechanisms rather than inside recovery documents.
The runbook should explain how authorized operators can re-establish access without depending on one employee’s laptop or memory.
Include Metadata and Governance
Important metadata may include schemas, views, roles, grants, policies, tasks, procedures, semantic definitions, lineage and schedules.
Determine which objects can be recreated from code and which need separate protection.
Security configuration deserves special care. A fast recovery that restores broad access accidentally is not successful.
Test least-privilege roles after recovery, particularly for sensitive schemas and service identities.
Map BI Dependencies
The warehouse is usually upstream of many reports.
Create a dependency map for critical dashboards and semantic models:
- which curated tables they use;
- how often they refresh;
- which credentials or gateways they require;
- which reports are business-critical;
- which applications embed analytics.
Actiknow’s single-source-of-truth guide notes that centralized analytics increases the impact of failure and recommends designing for warehouse, connector, transformation and source-schema failures from the beginning.
That dependency awareness should feed the recovery sequence.
Define the Recovery Order
A typical sequence is:
- contain the incident;
- identify the last known good state;
- restore or repair source connectivity;
- restore warehouse objects and data;
- restore transformations and orchestration;
- validate security;
- rebuild or refresh curated models;
- refresh semantic models;
- validate critical dashboards;
- communicate recovery status.
The actual sequence should reflect your architecture.
The important point is that it is written and tested before the incident.
Validate Data After Recovery
A technically successful restore can still contain wrong data.
Run reconciliation checks on critical datasets.
Compare row counts, totals, freshness and representative records. Re-run important data-quality tests. Validate key business metrics against an agreed source.
Do not reopen executive reporting simply because tables exist again.
Plan for Partial Operation
Sometimes full recovery takes longer than the business can wait.
Define which services can return first.
Perhaps a core finance dataset and executive dashboard can be restored before lower-priority historical models. Perhaps operational users can temporarily use a source-system report while the warehouse is rebuilt.
Prioritization should follow business impact.
Test the Plan
An untested recovery plan is a document, not a capability.
Schedule recovery exercises.
Test different scenarios, not only one happy-path restore. Measure actual recovery time. Record missing permissions, undocumented dependencies and manual steps.
After each exercise, update the runbook.
A useful test asks:
Can someone other than the original engineer execute the recovery?
If not, the process still contains key-person risk.
Monitor Recovery Readiness
Recovery readiness can degrade silently.
New pipelines appear. Credentials change. Reports are added. Data volumes grow. Retention settings change.
Review the recovery inventory periodically and after major architecture changes.
Track whether critical assets have owners, whether restore procedures are current, whether recent tests met RPO/RTO targets and whether unresolved recovery gaps remain.
Cloud-Native Features Are Inputs, Not the Whole Plan
Snowflake, BigQuery, Redshift and other platforms provide different native capabilities for retention, replication, backup and recovery.
Use the capabilities appropriate to your platform, but do not let the product feature list become the disaster recovery plan.
The plan must still cover people, process, code, credentials, dependencies, validation and communication.
Actiknow’s Snowflake vs BigQuery vs Redshift guide recommends evaluating governance, environment separation, access and the broader engineering ecosystem around the warehouse. Recovery planning should use the same whole-system perspective.
Frequently Asked Questions
Do cloud data warehouses need backups?
They need a recovery strategy. Native retention and recovery features may cover some scenarios, but organizations still need to plan for logical errors, pipelines, code, credentials, security and downstream dependencies.
What is RPO?
Recovery Point Objective is the maximum acceptable amount of data loss measured in time.
What is RTO?
Recovery Time Objective is the maximum acceptable time to restore a service after disruption.
Should every dataset have the same RPO and RTO?
No. Set objectives according to business criticality and the cost of data loss or downtime.
How often should disaster recovery be tested?
Use a cadence appropriate to business risk and test after material architectural changes. Critical systems generally justify more frequent exercises.
What should be restored first?
Restore the minimum set of sources, data, transformations, security and BI dependencies required for the highest-priority business services.
Does replication replace disaster recovery?
No. Replication can be an important component, but it can also replicate bad changes. Recovery still requires versioning, validation and an incident process.
Conclusion
Cloud data warehouse disaster recovery is an end-to-end operating capability.
Define business RPO and RTO. Plan for logical failures as well as outages. Protect data and code. Inventory credentials. Map downstream BI. Establish recovery order. Validate restored metrics. Test the runbook.
If your warehouse has become business-critical, Actiknow can help review the data, pipeline and BI dependencies that need to be included in a practical recovery plan. Discuss your data platform requirements with Actiknow.