| id | SKL-retention-RETENTIONARCHIVAL |
| name | Retention Archival |
| description | Policy and automation for data retention (how long to keep data), archival (move to cold storage), and deletion (permanently remove). This skill enables compliance with GDPR/CCPA retention limits, cos |
| version | 1.0.0 |
| status | active |
| owner | @cerebra-team |
| last_updated | 2026-02-22 |
| category | Backend |
| tags | ["api","backend","server","database"] |
| stack | ["Python","Node.js","REST API","GraphQL"] |
| difficulty | Intermediate |
Retention Archival
Skill Profile
(Select at least one profile to enable specific modules)
Overview
Policy and automation for data retention (how long to keep data), archival (move to cold storage), and deletion (permanently remove). This skill enables compliance with GDPR/CCPA retention limits, cost optimization through cold storage, improved query performance, and legal right to deletion.
Why This Matters
- Compliance: GDPR, CCPA retention limits
- Cost: Cold storage is cheaper than hot storage
- Performance: Less data = faster queries
- Legal: Right to deletion
Core Concepts & Rules
1. Core Principles
- Follow established patterns and conventions
- Maintain consistency across codebase
- Document decisions and trade-offs
2. Implementation Guidelines
- Start with the simplest viable solution
- Iterate based on feedback and requirements
- Test thoroughly before deployment
Inputs / Outputs / Contracts
- Inputs:
- Retention policy configuration
- Table schemas with retention requirements
- Archive storage configuration (S3, Glacier)
- Deletion request data
- Entry Conditions:
- Archive storage is configured and accessible
- Database supports partitioning
- Retention policy is documented and approved
- Outputs:
- Archived data in cold storage
- Deleted data from hot storage
- Audit logs for all operations
- Retention compliance reports
- Artifacts Required (Deliverables):
- Retention policy document
- Archival scripts
