Five Reasons Your Data Pipeline Fails Overnight

If you own a batch pipeline you know the feeling of opening your laptop to a wall of red tasks. Most overnight failures fall into a handful of categories.

1. Upstream data arrived late or not at all

Pipelines that assume a file or table is ready at a fixed time are fragile. Use sensors or explicit readiness checks instead of fixed schedules where you can.

2. The schema changed

A new column, a renamed field or a changed data type can break loads. Schema validation at ingestion turns a confusing downstream failure into a clear early error.

3. Data volume grew

Jobs that ran comfortably for months start timing out or running out of memory. Track run duration and input volume so that you see the trend before it becomes a failure.

4. Non-idempotent reruns

Rerunning a failed job should not create duplicates. Design loads so that they can be repeated safely — for example by using merge/upsert logic or partition overwrite.

5. Expired credentials and quotas

Tokens, passwords and certificates expire; API quotas get exhausted. Put expiry dates in a calendar and alert on authentication errors separately.

For help diagnosing a specific pipeline, see our data engineering job support.