Why a written backup strategy matters
Backups usually accumulate rather than get designed. Someone enables automated database snapshots, another team copies files to a bucket, and a third forgets that the queue and the secrets store exist. When a real incident arrives, you discover coverage by accident: the main database is protected, but the uploaded customer files, the encryption keys and the Kubernetes volumes are not.
A backup strategy is the document and the automation that answer four questions on purpose: what is protected, how often, where the copies live, and how long they are kept. It is the practical output of your recovery objectives and the foundation for everything else in our backup and disaster recovery service.
What good looks like
- Every data store appears in an inventory with an owner and a classification.
- Frequency matches the RPO of its tier, and you can show the evidence.
- At least one copy sits outside the blast radius of the production account.
- Copies cannot be deleted by the same credentials that run production.
- Retention is deliberate, not a default nobody chose.
- Restores have been tested, so the strategy has proof behind it.
How it works in practice
Step one: classify what you hold
We sort data into three groups. Irreplaceable data is anything you cannot regenerate: customer records, transactions, uploaded files, audit logs, signing keys. Rebuildable data can be reconstructed from other sources, such as search indexes built from a database. Disposable data, like caches and build artefacts, needs no backup at all.
This single step usually reduces scope. Teams often back up everything equally, paying for copies of data they could regenerate in minutes, while under-protecting the few stores that matter.
Step two: infrastructure is rebuilt, data is restored
If your environment is defined in infrastructure as code, the repository is the backup of your infrastructure. Recovery means applying the code to a clean account or region, then restoring data into it. That is far faster and more reliable than restoring machine images. It also means the real backup problem is smaller: state, data and secrets. If part of your estate was created by hand, see how we approach moving from ClickOps to Terraform first, because undocumented infrastructure is hard to recover.
Step three: frequency from RPO
Frequency is derived, not chosen by habit. A rough mapping:
| Recovery point needed | Typical mechanism |
|---|---|
| Minutes or less | Continuous log archiving or point-in-time recovery, plus replication |
| Hours | Frequent snapshots or incremental backups |
| A day | Nightly backup job |
| Days | Weekly backup, sometimes enough for low-tier data |
Databases deserve extra care. A nightly dump alone gives an RPO of up to a day. Point-in-time recovery from write-ahead or transaction logs is what closes that gap, and we cover the mechanics in database backup automation.
Step four: where the copies live
Think in failure domains. A snapshot in the same account and region protects against a bad deployment, not against a deleted account, a compromised administrator or a regional outage. A strong design keeps copies in layers: the primary data, a local recovery point for fast restores, a copy in another region, and a copy in a separate, restricted account. The mechanics are in cross-region and cross-account backup replication.
The gaps teams forget
These are the items we find unprotected most often:
- Object storage. Versioning protects against overwrite, but not against deletion of the bucket or an account compromise.
- Persistent volumes in Kubernetes. Manifests come from Git, yet volume data does not. See persistent volumes and Kubernetes consulting for how stateful workloads fit in.
- Secrets and keys. A restored database is useless if the key that decrypts it has been lost. Keys need their own recovery plan.
- SaaS data. Source control, ticketing and identity providers hold business-critical data that your cloud backups do not touch.
- Configuration outside code. DNS records, feature flags and third-party settings.
Decisions and trade-offs
Native tools or third-party software. For most AWS and Azure estates, native services cover the need with less to operate. A third-party tool makes sense when you span many clouds and need one control plane, or when you need features the native option lacks.
Snapshots or logical backups. Snapshots are quick and cheap to take. Logical exports are portable across versions and engines but slower to restore. Many teams use both.
How long to keep data. Longer retention is not free and is not always safer. Some data must be deleted on request, which sits uneasily with years of backups. We make retention a documented decision, and we examine it under cost and retention optimisation.
When not to over-engineer. A small product with one managed database may only need automated backups with point-in-time recovery, one cross-region copy and a quarterly restore test. A complex strategy that nobody maintains is worse than a simple one that is tested.
Common failure modes
- Backups that live next to the thing they protect. Same account, same credentials, same fate.
- No monitoring of backup jobs. A job fails for weeks and nobody notices.
- Restore never tried. The first restore happens during the incident.
- Retention set by default. A short default quietly limits how far back you can go.
Delivery and handover
You receive the strategy document, a data inventory, and policies implemented as code in your repositories. We set up alerts for failed or missing jobs and a dashboard showing coverage. Handover includes a walk-through for your engineers and a checklist for adding new data stores to the plan. The restore testing programme then validates it on a schedule.
How to tell it is working
Every irreplaceable store is covered, backup jobs succeed and alert when they do not, recent restores meet the target recovery times, and a new service cannot reach production without a backup policy. To get a second pair of eyes on your current setup, contact us.