Title: Transporting Content: Moving SAP Analytics Cloud (SAC) Objects Between Environments
Subject: SAP-Analytics-Cloud in the SAP Field
In any enterprise analytics setup, managing the lifecycle of analytical content—such as stories, models, data connections, and planning templates—is crucial for maintaining quality and consistency. In the SAP Analytics Cloud (SAC) environment, transporting content between different systems like Development, Test, and Production is a key activity that ensures controlled deployment and collaboration.
This article provides an overview of how to effectively transport SAC objects between environments, highlighting tools, best practices, and governance considerations.
Transporting content across environments supports:
- Quality Assurance: Develop and test new analytics content in non-production systems before release.
- Collaboration: Enable multiple teams to contribute and validate analytics artifacts.
- Governance: Maintain control over what gets promoted to production to avoid errors and inconsistencies.
- Version Control: Track changes and maintain backups across system landscapes.
Typical objects involved in transport include:
- Stories and Analytic Applications
- Models (including planning and calculation views)
- Data connections and import data files
- Planning sequences, input tasks, and process chains
- Roles and permissions (managed separately via SAP Identity Management)
SAP Analytics Cloud supports a Content Network feature that enables content sharing and transport between SAC tenants:
- Share models and stories between SAC systems by subscribing to a content network.
- Facilitate content reusability and collaboration.
- Limitations include manual approval and subscriber control over imported content.
¶ 2. Export and Import via Files
- SAC allows exporting objects such as stories and models as files (e.g., .json).
- Exported files can then be imported into another SAC tenant.
- This method is suitable for one-off transfers or smaller content sets but lacks automation and version control.
- For enterprises using SAP Cloud ALM or SAP Solution Manager, integration with SAC lifecycle management tools helps orchestrate content deployment.
- These tools provide automation, audit trails, and approval workflows for transporting SAC objects.
- Define development, test, and production tenants.
- Establish a formal process for content promotion and approval.
¶ 2. Maintain Version Control and Backups
- Regularly export and archive SAC objects before changes.
- Use descriptive version names and comments to track changes.
- Validate functionality, performance, and security settings.
- Engage business users for acceptance testing.
¶ 4. Manage User Roles and Permissions Separately
- Synchronize roles and authorizations via SAP Identity Management or manual processes.
- Ensure users have appropriate access in each environment.
¶ 5. Document Changes and Transport Activities
- Maintain logs of what was moved, by whom, and when.
- Use this documentation for auditing and troubleshooting.
¶ Common Challenges and How to Address Them
- Data Connection Dependencies: Ensure that data sources and connections exist and are correctly configured in the target environment.
- Version Conflicts: Coordinate content updates to avoid overwriting or conflicting versions.
- Security and Compliance: Validate that transported content adheres to organizational data governance policies.
- Limited Automation: For large-scale transport needs, integrate SAC lifecycle management with enterprise ALM tools.
Effective transport of SAP Analytics Cloud content between environments is essential for maintaining high-quality analytics solutions and enabling enterprise collaboration. By leveraging SAC’s content network, export/import functions, and integration with ALM tools, organizations can implement robust lifecycle management practices that reduce risk and increase agility.
Organizations should complement technical processes with governance policies, documentation, and user training to ensure smooth transitions of content through the analytics lifecycle.