Open Data Infrastructure
Disaster Recovery and Backup for the Open Lakehouse
Backing up files is not enough. You need a recovery plan for table metadata, catalogs, identities, and the operational workflows that keep the lakehouse consistent.
The worst day to learn what your lakehouse depends on is the day you have to rebuild it.
Why lakehouse DR is tricky
A lakehouse looks simple if you squint: data files in object storage plus metadata that points to them. Disaster recovery gets hard because production behavior depends on more than files. It depends on table history, catalog state, credentials, policies, and the ability to re-run the workflows that keep tables consistent.
Core idea: recovery is not "restore files." Recovery is "restore the contract" so engines and governance behave the same way again.
The layers you must recover
A credible DR plan explicitly covers:
- storage: object storage replication, versioning, and retention controls
- table metadata: the table format metadata and its history (snapshots, manifests, and retention policy)
- catalog: namespace state, table registrations, configuration, and operational logs
- identity and policy: service principals, access policies, and audit requirements
- pipelines: the orchestration and configuration required to resume writes safely
If you only back up data files, you will restore bytes without restoring meaning. That is not an ODI-aligned recovery posture.
RPO, RTO, and what you actually promise
Two definitions that tend to get hand-waved:
- RPO (recovery point objective): how much data loss you can tolerate
- RTO (recovery time objective): how long you can be down
In an open lakehouse, these are not only storage questions. They are also metadata and pipeline questions. A low RPO with a high RTO can still be unacceptable if your business depends on near-real-time decisions. A low RTO with a high RPO can still be unacceptable if you cannot reconcile the loss.
A runbook that survives an incident
A runbook that works includes concrete steps and proof points:
- restore storage access and verify critical buckets or prefixes
- restore catalog services, then validate authentication and authorization flows
- validate table metadata integrity and expected snapshot history
- bring up read-only access first, then controlled writes
- replay critical pipelines with guardrails, then expand
Every step should have a verification query or check. If a step cannot be verified, it is not a step. It is a hope.
How to test without lying to yourself
DR plans fail when testing is theoretical. Practical testing looks like:
- regular restore drills on representative tables, not only toy datasets
- explicit tests for table history and time travel behavior after restore
- tests for policy enforcement and audit logging after restore
- rehearsed procedures for "bad write" recovery, not only region failure
If you are running multiple engines, include them in the drill. A restore that works for one engine and fails for another is not a restore. It is a partial outage.
Sources to start with
Start with the table format contract and the catalog protocol contract, then design DR around those realities.