Data protection is critical for any development team leveraging Azure DevOps to manage code, automate builds and releases, and track work. However, relying solely on redundancy within Azure DevOps’ hosting environments is insufficient—proactive backup strategies are needed to safeguard against data loss from platform outages and accidental user deletions.
This blog post outlines five essential approaches every organization should adopt as part of a comprehensive Azure DevOps data insurance plan. We’ll explore how to export critical build definitions, backup source code repositories, safeguard work items and documentation and restore from backups. As importantly, we’ll discuss integrating backups directly into the existing DevOps automation pipelines for consistency and oversight.
Development teams can regain control over their most valuable data assets by operationalizing regular exports, mirrors, restore tests, and automated backup jobs. This protects against large and small disasters and ensures all the components powering DevOps workflows remain safely recoverable for the long term.
1. Export Build and Release Definitions
One of the most critical assets in Azure DevOps are build and release definitions, which contain the steps, tasks, and configurations that automate your software build, test, and deployment processes. Losing these definitions could mean losing the ability to automatically build and release software updates.
Fortunately, build and release definitions can be exported from the Azure DevOps web interface into JSON files that can be backed up independently of the DevOps environment. This allows definitions to be restored even if the underlying Azure DevOps organization was lost.
To export definitions:
- Navigate to the Pipelines page in DevOps and select a build or release definition
- Click the three-dot menu and select “Export pipeline.”
- Save the exported JSON file to a secure backup location like your version control system
Be sure to schedule regular exports of ALL build and release definitions as part of your backup routine. Consider automating this export process to run nightly or weekly.
2. Backup Code Repositories
The source code stored in your Azure DevOps repositories is critical data that needs robust backup protections. Thankfully, Azure DevOps leverages underlying Git repositories, which allow you to export repositories as backups.
The most straightforward approach is configuring a secondary “mirror” repository in another dedicated backup Azure DevOps organization or third-party Git hosting provider like GitHub. This mirror repository can then be kept in sync via scheduled push operations.
Consider exporting entire repository bundles to securely stored backup locations for even more robust backups. To export a repository bundle:
- In Azure DevOps, navigate to the repository overview page
- Click the three-dot menu and select “Export repository.”
- Save the exported .zip repository bundle file to secure backup storage
Be sure to schedule backups of all code repositories on a regular cadence appropriate for your development workflow. Backups before major releases are also recommended.
3. Backup Work Items, Plans, and Wiki
While less critical than code and pipelines, work items, plans, and internal documentation stored in project Wikis still represent necessary development metadata. Any accidental changes or deletes could result in lost context or work without backups.
Fortunately, Azure DevOps allows exporting these items to JSON files for easy backup and restore.
To export:
- Navigate to the Queries page and select “Export query results” for any relevant work item queries
- On the Boards page, export any backlogs or boards using the “Export” option under processes
- From project settings, select “Export” under the Wiki section
Save the exported JSON files locally and regularly to your backup storage locations along with code and pipeline backups.
4. Restore Environment from Backups
The best insurance against data loss is having a process for rapidly restoring your Azure DevOps environment from backups. This process should be regularly tested to validate backup integrity.
To restore from backups:
- Create a new “restore” Azure DevOps organization
- Import exported code repository bundles via the Import repository feature
- Import exported pipeline definitions via the Pipelines -> Import function
- Import and export work item, board, wiki JSON data
- Verify all environments, pipelines and functionality is restored correctly
Regular point-in-time restore tests help validate that no critical data is missing from backups and the overall restore process is well documented for disaster scenarios.
5. Integrate Build/Deploy Automation
The most robust backup approach integrates export/restore directly into your development, build, and deployment automation pipelines, automating the entire data protection lifecycle.
Example pipeline steps:
- During nightly builds, trigger an export of the latest code/pipelines
- Push code repo exports to backup storage with DevOps credentials
- On deployment pipelines, include a validation step to test restoring exports
- Build a scheduled trigger to run point-in-time restores daily/weekly
Some key benefits of pipeline integration include:
- Ensuring backups are always in sync with deployments
- Automating regular restore tests for validation
- Providing audit logs of backup/restore executions
- Integrating failed restores into issue/alerting systems
With Azure DevOps, the automation capabilities allow data protection to be raised to the same level as your core applications.
Conclusion
By combining exports, mirrors, restoration testing, and automation through pipelines, organizations gain a multi-layered data protection strategy resilient to expected and unexpected failures. These five pillars lay the foundation for an effective Azure DevOps backup regimen, but every team’s needs will vary—the overall approach requires custom tailoring and ongoing optimization.
What remains consistent is the necessity of operationalizing backups through automation and regular testing. Just as code quality is validated through pipelines and environments, teams must validate the recoverability of critical DevOps data assets.
