Data Corruption – When Your Data Becomes Unreliable

The Scenario: The App Works, But the Data Lies

Your users’ data is holy: Typically most of your business is centered around handling and protecting data. Unfortunately, at one point your users start noticing strange behavior in your application: missing fields, incorrect values, maybe even crashes. Confused and frustrated, they open support tickets. You dig into the backend and realize the problem isn’t the code: it’s the data.

Something went wrong during a recent migration, schema update, or processing job. Maybe a script had a bug. Maybe a step was skipped. Whatever the root cause, the result is clear: your data is no longer reliable, and trust in your application is starting to erode.

The Quick Fix: Contain the Damage

When corruption hits, your goal is to stop it from spreading and start fixing what’s broken:

• Restore from backup: If recent backups are available, roll them out to recover known-good data.
• Manual correction: When backups are outdated or incomplete, you may need to manually patch up key data.
• Enable maintenance mode: Temporarily take the app offline or put things into read-only mode to prevent further damage while repairs are in progress.

Speed matters here, but so does communication: Let users know what’s happening and what to expect while you work on recovery.

Understanding the Pattern: Fragile Processes, Big Consequences

Data corruption is rarely random. It tends to follow a pattern: a change is made – whether through migration, update, or automation – and validation is either skipped or insufficient. The system continues running, but with broken data under the surface.

The corruption may not show up immediately. Sometimes it takes days or weeks before someone notices. But by then, the damage has already spread. In many cases, these issues go unnoticed because teams focus testing on application logic, not on the data itself. The lesson: every operation that touches your data is a potential failure point and should be treated as such.

The Long-Term Fix: Make Your Data Safer by Design

Corruption can’t always be avoided, but it can be anticipated. To reduce risk and recover faster in the future, strengthen your data handling processes:

• Test migrations and scripts thoroughly: Run every migration or update in staging first and test carefully before touching live data.
• Validate data before and after changes: Add integrity checks to catch problems early.
• Automate backups: Run them regularly and verify that restores work.
• Build rollback procedures: Give yourself a safe path back when something goes wrong.

These aren’t just nice-to-haves – they’re critical safety nets: With the right processes in place, data corruption doesn’t have to become a disaster.

In Short

Data corruption is quiet, but dangerous. One faulty script or botched migration can erode data quality, crash features, and damage trust. It often sneaks in after schema updates or processing jobs, when validation is weak or nonexistent. Quick recovery starts with good backups and isolation strategies like maintenance mode. Long-term resilience means testing, validation, and proactive monitoring.

If your app depends on its data (and it does), protecting it should be part of your core engineering practice – not an afterthought.

Stay tuned, Matthias

This blog post is part of our multi-part series, where we describe common software outages and help you resolve them quickly. You can find all other posts under Foreword: Navigating the Storms of Software Outages.