More Control, Less Hassle: Self-Serve Recovery with Point-in-Time Restore

A duck that never forgets

Your MotherDuck warehouse now has a time machine. Every time the database checkpoints, MotherDuck creates timestamped, automatic snapshots of attached databases in the background by default. Each snapshot captures the complete state of the database at a point in time and is retained for the duration of your database's retention_days, which determines its historical snapshot retention policy.

Users can create manual snapshots at any time that are subject to the database's snapshot retention window:

CREATE SNAPSHOT OF analytics_prod;

Users may also choose to create named snapshots for easier retrieval:

CREATE SNAPSHOT 'prod_backup_feb_2026' OF analytics_prod;

Named snapshots are durable, long-lived recovery points for your data. In MotherDuck, a named snapshot persists even if you delete the source database; a snapshot will not be garbage-collected or deleted unless you remove its name:

ALTER snapshot 'prod_backup_feb_2026' SET snapshot_name = '';

Once deleted, the snapshot will move through the storage lifecycle according to the specified snapshot_retention_days set at the database level.

These details can be found in the databases information schema, md_information_schema.databases:

FROM md_information_schema.databases
ORDER BY created_ts DESC;

Running this command returns the following results:

Copy code
| name                     | uuid                                 | created_ts              | transient | historical_snapshot_retention | type      |
|--------------------------|--------------------------------------|--------------------------|-----------|-------------------------------|----------|
| prod_analytics           | f0eb514d-2b6b-4ac3-a09d-400398195bb3 | 2026-02-01 14:46:00 -05 | false     | 30 days                        | DEFAULT   |
| prod_analytics_v2        | 7dcba482-15ac-4e46-80e4-239d9c7e3d71 | 2026-02-03 19:38:17 -05 | false     | 60 days                        | DEFAULT   |
| staging_analytics        | 3b6c8d72-6652-4f51-a308-cf31bfbe2897 | 2026-02-01 17:07:14 -05 | true      | 5 days                         | DEFAULT   |
| staging_analytics_v2     | f369b586-cb44-46c8-b28d-c03160266b7d | 2026-02-03 17:07:52 -05 | true      | 5 days                         | DEFAULT   |
| lakehouse_prod           | a07a0ed0-5fa6-45c6-9a7f-463e745aaf0c | 2025-07-31 05:17:57 -05 | false     | 00:00:00                      | DUCKLAKE  |

Designed as intentional, long-lived backups, named snapshots can be directly referenced by name during point-in-time recovery or clone operations:

ALTER DATABASE analytics_prod SET SNAPSHOT TO (SNAPSHOT_NAME 'prod_backup_feb_2026');

Alternatively, users can apply the ALTER SNAPSHOT command to an existing snapshot to add a name:

ALTER SNAPSHOT 'prod_backup_feb_2026' SET snapshot_name = 'stable_before_schema_change_feb_2026';

While automatic snapshots serve as a rolling safety buffer, named snapshots function as explicit recovery contracts for future use to safeguard production deployments against accidental missteps.

A Realistic Recovery Story

It's 10:12 a.m. You're about to ship a schema migration to your production analytics database. It's been tested. It looks fine. It still makes you nervous.

So you do the responsible thing and take a named snapshot:

CREATE SNAPSHOT 'stable_before_schema_change_feb_2026' OF analytics_prod;

You deploy.

Five minutes later, someone posts in Slack:

A migration half-applied. A backfill ran with the wrong join. Or, a simple accident–someone dropped the wrong table.

It doesn't really matter what happened when the outcome is the same: production data is now wrong, and it's a moment where heroics, or guesswork, or more vibe-coding don't pass muster.

Data stewards want to inspect, validate, and recover, calmly and deterministically, and without making things worse.

The Recovery Flow

Restoring data in MotherDuck is designed around the following operational loop:

Create → Change → Inspect → Restore → Validate → Promote

More concretely:

Let's walk through each step with real commands.

Step 1: You can only expect what you can inspect

First, we'll check what snapshots exist in the database snapshots information schema, md_information_schema.database_snapshots:

FROM md_information_schema.database_snapshots
WHERE database_name = 'prod-analytics_US'
ORDER BY created_ts DESC;

If you know roughly when things broke, it's easy to filter by time.

Step 2: Safely restore to a new database

Instead of immediately rewinding production, restore it into a new database for validation to turn recovery into a predictable, testable workflow:

CREATE DATABASE analytics_recovery
FROM analytics_prod (SNAPSHOT_NAME 'pre_schema_v4');

Alternatively, you can use a snapshot ID for a very specific restore operation:

CREATE DATABASE analytics_recovery
FROM analytics_prod (SNAPSHOT_ID 'c204ce3b-f3fd-4677-8a05-e8680648cf27');

Restoring to a new database can help with validations and additional sense checks on row counts and schemas. As a final step, we can run critical queries and inspect result sets and summary statistics in the Column Explorer to confirm that our data looks correct.

Step 3: Promote the fix to production

Once you've confirmed the snapshot is the correct one, restore production in place:

ALTER DATABASE analytics_prod
SET SNAPSHOT TO (SNAPSHOT_NAME 'pre_schema_v4');

Alternatively, you may use a snapshot ID for additional precision:

ALTER DATABASE analytics_prod
SET SNAPSHOT TO (SNAPSHOT_ID 'c204ce3b-f3fd-4677-8a05-e8680648cf27');

Et voilà! Your database is now back to a known-good state without relying on external tools, support tickets, or guesswork.

What if someone drops a database?

Sometimes, mistakes are more dramatic - what happens when Claude drops Production?!

DROP DATABASE analytics_prod;

Thankfully, we can rewind without regret and UNDROP our database:

UNDROP DATABASE analytics_prod;

As long as the drop falls within your database's configured historical snapshot retention window, MotherDuck can restore the database and its snapshot history. Think of it as a safety net for your database that's fast, predictable, and self-serve.

In these scenarios, snapshots are especially useful for planned rollbacks and for recovering from accidental database deletions and automation errors, whether AI- or human-enabled.

Production-Grade CYA (in the best possible way)

Here are a few patterns we recommend as a set of guardrails to help you ship without turning every deploy into a high-stakes bet:

• Create named snapshots before risky operations like migrations, backfills, and permission changes

• Consider tuning your snapshot retention period based on how long it realistically takes your team to detect and respond to issues

• Automate snapshot creation in your deployment and migration workflows

• Restore into a new database first to validate your data before touching production

• Don't use transient databases for critical data that you may need to recover

Though backups you never need to restore are theoretical, the workflows we have covered are designed to make restores routine, testable, boring, and dead simple, which is exactly what you want in production.

Point-in-time restore in MotherDuck is:

• Inspectable and searchable due to system catalogs and information schemas

• Safe thanks to validation-first restore workflows

• Fast by using UNDROP DATABASE for error recovery

• Precise through offering user-configurable control over your data's history

Take your ducks back in time

Point-in-time restore is now available in MotherDuck. Users can now access a configurable retention period of up to 90 days of historical retention. We're so glad to bring this feature to MotherDuck users, and we hope it adds an extra layer of confidence to your queries, whether you're reverting a change you made on purpose or, well, you simply ducked up.