Disaster Recovery
Advanced 15 min read Disaster Recovery

Automation & Orchestration of DR

A plan that executes itself — mostly

DR as code: infra-as-code turn-up, runbook automation (promote, DNS flip, smoke), policy-driven backups, self-healing playbooks, and deterministic approvals. The gold: DR that runs mostly itself and always reports its state.

runbook automationIaC DRpolicy backupsself-healingplaybookdeterministicas-code
Prerequisites: DR Drills & Testing Infrastructure as Code
Before we start

What you will learn

  • Encode the DR turn-up as infrastructure-as-code
  • Automate promote/DNS/smoke steps safely
  • Gate destructive actions with approvals + audit
In 60 seconds

DR automation: IaC applies the standby in region-B; stacks for promote (DB promote, DNS flip), smoke, and failback; policy engines enforce backups; playbooks at an ops-runner with approval gates. Humans decide when; machines execute deterministically; state reports.

Introduction

What is this?

Tools: Terraform/CDK for DR stacks + drift detection; GitHub Actions / pipelines triggered by ops-runner (Hashicorp, AWS OpsAutomation, Azure Automation runbooks) for runbook steps; Route53/Traffic Manager DNS automation; DB promote via SDK/CLI (rds promote-read-replica / aurora failover); monitoring hooks (healthcheck-driven or manual 'declare DR' button). Self-healing: health-check auto-recovery within region (ASG) vs DR escalation script that pages owner on sustained regional impact. Policy/backup automation: AWS Backup plans + Org policies, Azure backup policies, GCP backup configs; enforcement + coverage reports. Safety: approval gates for promote/DNS flip (2-person or approval queue), dry-run mode, journal of every action, rollback steps, versioned playbooks in git, RBAC-scoped creds. Metrics: every DR action emits logs/metrics for the scorecard.

Motivation

Why do you need it?

  • Human recall under pressure causes the mistakes the drill keeps finding — automation removes the recall.
  • IaC makes 'region-B exists' deterministic and reviewable (drift is a code review away).
  • Policy-driven backups fix coverage gaps silently (new services auto-covered).
  • Automated reporting turns 'did we do the backup' to 'every backup, every region, proven'.
Think about it

Mental model

Imagine

A pilot's auto-takeoff with a trained pilot in the loop: automation flies the numbers, a human makes the call

Think of

DR automation

You'd never automate the whole landing without a human at the controls; the same for promote/DNS-flip. Automate the steps, gate the decision, journal every move.

Mechanics

How it works

  • IaC describes DR stack in region-B; CI keeps it drift-free and fresh.
  • Runbook steps encoded: promote DB, flip DNS, smoke → executed by the ops-runner.
  • Approval gate on the destructive steps; dry-run first; journaled actions.
  • Monitoring + policy reports run continuously (backup coverage, replication lag, standby health).
  • On declare-DR: exec playbook, stream stage-by-stage status to a dashboard, produce the scorecard.

DR orchestration stack.

IaC
Playbooks
Approval gate
Executor
Reports
Hands-on

Examples

DR playbook step (pseudo — HashiCorp/ops-runner)

DR playbook step (pseudo — HashiCorp/ops-runner)
steps:
  - name: "promote_db"
    when: "declared == true and approved == true"
    run: "aws rds promote-read-replica --db-instance-identifier dr-standby"
    ok_on: "exit 0"
    journal: "promote_db:started"
  - name: "flip_dns"
    run: "aws route53 change-resource-record-sets --change-batch <batch>"
    ok_on: "changed == true"
    rolls_back: "// restore primary batch"
  - name: "smoke"
    run: "curl -f --max-time 10 https://app.dr.example.com/healthz",
  - name: "report"
    run: "scorecard → metrics + slack"

Deterministic steps, approvals, journaling, and a smoke + report — the whole cutover is a one-trigger pipeline.

Real world

In production

  • Terraform modules for DR stack; nightly drift check reports to the dashboard.
  • Ops-runner playbook 'DR: promote' gated on 2-person approval, journaled.
  • AWS Backup org policy covering every account/region; coverage dashboard + alerts on gaps.
Guidelines

Best practices

  • Run DR as code: IAM, LB, DNS, certs all declarative in the standby region.
  • Automate the boring steps; keep a human approval on destructive acts (promote, DNS flip, migrations).
  • Journal everything; every playbook action feeds metrics for the drill scorecard.
  • Versioned playbooks in git, PR-reviewed like code; drill each release.
  • Idempotency + dry-run + rollback steps on every playbook (re-run safe after partial failure).
Watch out

Common mistakes

  • Fully automatic DR button with zero approval: one false trigger = self-inflicted switchover.
  • IaC drift left unchecked — region-B stack has rotted between drills.
  • Playbooks opaquely 'magic'; on-call can't explain or trust; scorecard untrusted.
  • Automated backups that silently stopped covering a new workload (coverage report missing).
When things break

Debugging tips

  • A DR playbook fails at step 3 → steps should be idempotent; add skip-if-already checks and retries.
  • Drift report red but stack looks fine → compare against deployed resources by arn/id, not just state file.
  • Approval gate freezes mid-process → journaling should let you resume from the failed step without re-running earlier actions.
  • Backup coverage gap last month → policy engine missed the account/region; add org-level enforcement + alerts.
Interviews

Interview checkpoint

Test yourself

Quick quiz

0/2
1

The step that should usually stay human-approved in DR automation is:

2

Why keep DR steps idempotent?

Go deeper

Official documentation

Terraform examples
docs
AWS Systems Manager runbooks
docs
Azure automation runbooks
docs