After orchestrating disaster recovery for over 15 production incidents, I’ve learned one thing: it’s not a matter of if a disaster will happen, but when. A solid DR plan is the difference between a 15-minute blip and a multi-day outage. This is my personal playbook for building resilient cloud infrastructure, based on real-world scenarios.
My Core DR Principles
My Foundation: I Define RPO and RTO for Every Service
The first step is always to define the business requirements. I learned this the hard way back in 2019 when our payment processing service went down for six hours. Nobody had ever bothered to ask the CFO how much downtime was actually acceptable. Turns out, “we need it back now” isn’t a useful technical requirement when you’re staring at a blown budget and a CEO who wants answers.
These days, I work with stakeholders to classify every service into tiers. For each tier, we agree on a Recovery Point Objective (RPO), which is the maximum acceptable data loss, and a Recovery Time Objective (RTO), which is the maximum acceptable downtime. A Tier 1 Critical service might need RPO under 5 minutes and RTO under 15 minutes. Tier 2 Important services can tolerate RPO under 1 hour and RTO under 4 hours. For Tier 3 Standard services, we’re comfortable with 24-hour windows for both. These numbers dictate everything else, from our backup frequency to how much we’re willing to spend keeping a warm standby running.
My Backup Strategy: Automate, Verify, and Layer
I’ll never forget our first major DR drill in 2020. We confidently triggered our failover to the backup region, only to discover that our database backups had been silently failing for three weeks. The backup script was running fine, but nobody had bothered to verify the uploads were actually completing. We spent that entire weekend scrambling to piece together data from application logs and cache layers. It was humiliating.
Now I’m obsessive about verification. For databases, I have an automated script that runs nightly to take a pg_dump of our production database, compress it, upload it to S3 with encryption, and then actually verify the upload succeeded and the file isn’t corrupted. For Kubernetes state, I use Velero to take daily snapshots of our cluster state, including all resource definitions and persistent volume data. I also wrote a custom Python BackupManager class for application-specific data, with built-in retention policies and verification checks that actually alert someone if they fail.
The lesson I learned the hard way is that backups you haven’t tested aren’t backups at all. They’re just files taking up storage space and giving you false confidence.
My High-Availability Architecture: Active-Passive Multi-Region
For our critical services, I run an active-passive setup across two AWS regions. The primary region handles all live traffic. In the secondary region, I keep a read replica of our RDS database and a scaled-down version of our application running. Route 53 health checks automatically fail over traffic to the secondary region if the primary becomes unhealthy.
This setup saved us during the AWS us-east-1 outage in December 2021. While other companies were tweeting apologies to customers, our traffic automatically shifted to us-west-2, and most of our users didn’t even notice. The whole failover took about 12 minutes. Our support team got maybe three confused tickets, all from users who happened to be refreshing their browser at the exact moment of the switch.
My Failover Process: Fully Automated
Here’s something nobody tells you about disaster recovery: manual processes fail catastrophically under pressure. I learned this during a 3 AM incident when our on-call engineer, half-asleep and panicking, ran the failover commands in the wrong order and took down both our primary and secondary regions simultaneously. That was a fun phone call to get.
After that nightmare, I wrote a Python FailoverOrchestrator that automates the entire failover process. When triggered, either automatically by an alarm or manually by an engineer, it verifies the primary is actually down, promotes the secondary database replica, updates the DNS records, and scales up the application in the DR region. The script includes safety checks at every step, so you can’t accidentally destroy both regions like we did that night. This automation is the only reason we can reliably meet our 15-minute RTO, because humans make terrible decisions at 3 AM.
My Golden Rule: Test Your DR Plan Relentlessly
An untested DR plan is a fantasy. I say this as someone who once had a beautifully documented 47-page DR runbook that turned out to be completely useless when we actually needed it. Half the commands were wrong, the DNS propagation took three times longer than documented, and our monitoring didn’t work in the failover region because someone had hardcoded the primary region’s endpoints.
Now we conduct a full DR drill every quarter. We simulate a primary region failure in our staging environment and execute our failover runbook. This is a mandatory, all-hands-on-deck exercise, and yes, people complain about the time investment. But it’s how we find the gaps in our plan and build the team’s muscle memory for a real event. During our last drill, we discovered that our new authentication service had a hidden dependency on a legacy API that wasn’t included in the failover automation. Finding that during a drill instead of a real outage probably saved us from a six-hour incident.
The Real-World Results
The proof is in the numbers. We successfully recovered from a full AWS region failure in just 12 minutes, beating our 15-minute RTO. We’ve had zero data loss in the last three production incidents. We’ve maintained 99.99% availability for our critical services for over two years. And perhaps most surprisingly to management, we reduced our DR costs by 60% by using a warm standby strategy instead of a fully active-active hot standby. Turns out you don’t need two identical production environments running 24/7 if you architect things properly.
My Key Takeaways for Disaster Recovery
Define your RPO and RTO first. Everything else flows from these business requirements, and you need actual numbers from actual stakeholders, not guesses from the engineering team.
Automate everything. You can’t rely on manual steps in a crisis. Your failover process should be a single command or button press, with safety checks built in to prevent the 3 AM disasters I mentioned earlier.
Test regularly and realistically. If you haven’t tested your DR plan in the last quarter, you don’t have one. You have a document that makes you feel better but won’t help when things break.
Document everything, but keep it concise. A clear, focused runbook is invaluable when things go wrong. That 47-page document I mentioned? Nobody read it during the incident. Our current runbook is six pages, and everyone on the team has practiced it multiple times.
Finally, practice failback. Getting back to your primary region is just as important and often more complex than the initial failover. We learned this when we failed over to our DR region for a planned maintenance window and then spent eight hours trying to fail back because we’d never actually tested that direction. Don’t make the same mistake.