# Data Freshness

> A successful pipeline run can still leave users with stale data. Check the latest timestamp, expected partition, row volume, source delay, and freshness SLA before treating the result as ready.

September 2026 | Data Quality

Tags: Data Freshness, Monitoring, SLA

Canonical page: https://datasketch.caesarmar.io/sketches/data-freshness/

![Freshness checks after pipeline success, with publish and notify or alert, investigate, and communicate outcomes](https://datasketch.caesarmar.io/images/data-sketch/data-sketch-episode-08.jpg)

## Reading the sketch

### Check the latest timestamp

Inspect how recent the available data is, even when the pipeline reports success.

### Check the expected partition

Confirm that the partition needed for this delivery is present.

### Review the row volume

Check the amount of data received for the expected delivery.

### Check for source delay

Determine whether the source has delivered the data required for the refresh.

### Compare with the freshness SLA

Check whether the data meets the agreed freshness requirement.

### Choose the delivery outcome

Publish and notify users when the checks pass. If a check fails, alert, investigate, and communicate the delay.

## Takeaway

Use freshness checks alongside pipeline status. Users need to know whether the expected data arrived and meets the delivery requirement.

## Sources

- [Original LinkedIn post](https://lnkd.in/p/gdfHCzTz)
- [Mario Caesar](https://caesarmar.io/index.md)