A production-focused engineering reference for proving — on a schedule — that your backups actually restore.
Disaster recovery has moved from a periodic compliance checkbox to a continuous engineering discipline. This site translates recovery objectives into measurable, repeatable outcomes: immutable storage architecture, deterministic validation pipelines, and stateful drill orchestration that runs without manual intervention. Every guide is hands-on and Python-first — copy-ready validation scripts, orchestration patterns, and infrastructure-as-code you can adapt to PostgreSQL, MySQL, MongoDB, and Kubernetes-based platforms.
It is written for database administrators, site reliability engineers, disaster recovery planners, and Python automation engineers who own recoverability as a production SLA and need executable answers, not marketing prose.
The library is 52 in-depth guides organised into four areas, each moving from architecture to runnable implementation to verification and failure modes:
| Area | What it covers |
|---|---|
| Core DR Architecture & Validation Fundamentals | Storage taxonomy and tiering, RTO/RPO mapping as pipeline inputs, validation-model selection, zero-trust security boundaries, and continuous replication validation. |
| Automated Backup Integrity Check Implementation | Checksum validation pipelines, page-corruption scanning, error categorization, async batching for large datasets, and cryptographic manifest signing. |
| Restore Drill Orchestration & Environment Isolation | Sandbox provisioning, point-in-time recovery targeting, smoke-test routing, fallback chains, and drill data masking & anonymization. |
| Recovery Validation Telemetry & Compliance | Validation telemetry and metrics, compliance evidence and control mapping (NIST SP 800-34, SOC 2, ISO 22301), immutable audit trails, and drill scheduling & orchestration. |
Highlights include direct engineering comparisons (SHA-256 vs BLAKE3, Airflow vs Celery), engine-specific implementation guides for PostgreSQL WAL streaming, MySQL replica lag, and MongoDB oplog validation, and end-to-end scripts that gate DR pipelines on explicit POSIX exit codes.
- Runnable, not illustrative. Every code block is complete, syntax-checked Python — no pseudocode, no ellipses. Scripts that gate a recovery step return real exit codes an orchestrator can branch on.
- Engineering constraints, not aspirations. Recovery objectives are treated as inputs that shape pipeline topology, backup cadence, and compute budgets.
- Auditable by design. Telemetry, control mapping, and tamper-evident audit trails turn "we ran a drill" into evidence an auditor and an on-call engineer can both rely on.
- Accessible and fast. Hand-authored inline diagrams, responsive tables, and a lean, dependency-light front end that works offline.
- Eleventy static site generator (Nunjucks + Markdown)
- Hand-authored inline SVG diagrams and a custom, theme-aware design system
- Deployed on Cloudflare Workers with a service worker for offline support
Start at www.backupvalidation.org and dive into whichever area matches the problem in front of you. Each guide cross-links to the adjacent topics it feeds or depends on, so you can trace a concern from architecture all the way down to the script that proves it.