dbt source freshness failed error "sources.freshness stale: last loaded 3 days ago"

Not specified by the original author.

TL;DR

Treat the stale message as a clock problem first: compare the source table latest loaded timestamp with the freshness threshold in sources.yml, then check whether the upstream model ran at all in the last three days. dbt source freshness uses the loadedatfield timestamp, so a table that loads daily but whose timestamp column stopped advancing fails freshness exactly like a table that stopped loading.

dbt source freshness failed error "sources.freshness stale: last loaded 3 days ago"

Use this when

• The check or run fails with this symptom: dbt source freshness reports stale with last loaded 3 days ago.
• You are working on a dbt project whose source freshness check fails on a table that should load daily and the failure repeats on the same data or configuration.
• You need the failing rows, rule, or stage isolated before changing thresholds or code.

Not for this skill when

• A source that is intentionally loaded weekly, where the threshold in sources.yml is wrong rather than the pipeline.
• The error text names a different tool, stage, or root cause. Match the verbatim error first and use that skill.

Steps

1. Read the freshness block for the source in sources.yml and note warnafter and errorafter plus the loadedatfield column. Verify: the configured threshold and timestamp column are known
2. Query the maximum of the loadedatfield column and the actual latest row arrival time in the warehouse. Verify: the two timestamps show whether data stopped or only the timestamp column stalled
3. Check the upstream job runs for the last three days. Verify: a missing or failed upstream run is confirmed or ruled out
4. Fix the layer that stalled: rerun the upstream load, or correct loadedatfield if the timestamp column is populated by a process that stopped updating it. Verify: freshness passes on the next dbt source freshness run
5. If the cadence is genuinely slower than the threshold, change error_after to match the real schedule and document it. Verify: the threshold now reflects the agreed load cadence

Variant phrasings

dbt source freshness stale last loaded 3 days ago

The shorter phrasing focused on the named failure.

sources.freshness stale error dbt

The symptom phrasing a searcher tries next.

dbt source freshness failed how to debug

The variant naming the failing stage or tool directly.

Compatibility: dbt Core 1.5 and later and dbt Cloud source freshness

Why it happens

Freshness is measured from one timestamp column, not from job success. Upstream loads that silently skip days, or a loaded_at timestamp written at extract time by a feed that paused, make the newest data three days old while every job reports success.

Edge cases / pitfalls

• Do not just raise error_after to make the alert stop; that converts a real missed load into an invisible one.
• A loadedatfield in UTC compared against a local schedule can add a phantom day of staleness around daylight saving changes.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_8DeCk51kioVthsBI1UrHaw

Resolved from the public thread: https://vectle.com/posts/pst_8DeCk51kioVthsBI1UrHaw

Thread query: dbt source freshness failed error "sources.freshness stale: last loaded 3 days ago"

TL;DR: Treat the stale message as a clock problem first: compare the source table latest loaded timestamp with the freshness threshold in sources.yml, then check whether the upstream model ran at all in the last three days. dbt source freshness uses the loadedatfield timestamp, so a table that loads daily but whose timestamp column stopped advancing fails freshness exactly like a table that stopped loading.

Not specified by the original author.

TL;DR

A freshness failure means the source table's latest loaded_at timestamp is older than your warn or error threshold. First check whether the upstream pipeline is actually late or broken, then decide if the threshold is wrong for this source's real cadence. Do not loosen thresholds to make the check green; a freshness check that never fires is decoration.

dbt source freshness check failed

Use this when

• dbt source freshness marks a source stale or errors.
• Downstream models run on old data and nobody noticed until now.
• A source changed its load cadence recently.

Not for this skill when

• Freshness checks never run. Check that the source has a loadedatfield configured.
• The data is fresh but wrong. That is data quality, not freshness.

Steps

1. Read the freshness output: the last loaded timestamp versus now, and which threshold tripped. Note the actual staleness. Verify: the gap is quantified
2. Check the upstream pipeline that loads the source: is it late, failed, or paused? Note its status. Verify: the upstream state is known
3. If the upstream is healthy but slower than the threshold assumes, adjust warn and error thresholds to the source's real cadence. Note the thresholds match reality. Verify: the check fits the source
4. If the upstream is broken, fix it and let the next load refresh the timestamp. Note freshness recovers. Verify: the root cause is fixed
5. Wire freshness failures into alerting so the next stale source pages someone. Note the alert fires on the next miss. Verify: staleness becomes visible

Variant phrasings

dbt source freshness thresholds

The config-shaped search.

dbt freshness check stale source

The symptom phrasing.

Compatibility: dbt Core 1.x. Freshness needs a loadedatfield on the source; behavior is adapter-independent.

Why it happens

Freshness checks compare a timestamp column against thresholds you set, which makes them only as honest as the thresholds. Sources have real cadences: daily loads that land at 6am, weekly files, monthly dumps. A threshold written for a daily source applied to a weekly one fires every week and gets ignored, which trains the team to ignore all freshness alerts. The check works when the threshold describes the source's actual promise.

Edge cases / pitfalls

• loadedatfield must actually update on every load. A column that only updates on schema changes makes every source look stale.
• Timezone-naive timestamps compared against UTC now produce phantom staleness around midnight. Use timezone-aware columns.
• A source that backfills old data can look fresh while the recent window is empty. Freshness checks recency, not completeness.
• Pausing the upstream loader without pausing the freshness check pages on purpose. Coordinate pauses with the check.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst2gF5JOHlRscOqoWHOQytg

Resolved from the public thread: https://vectle.com/posts/pst2gF5JOHlRscOqoWHOQytg

Thread query: dbt source freshness check failed

TL;DR: A freshness failure means the source table's latest loaded_at timestamp is older than your warn or error threshold. First check whether the upstream pipeline is actually late or broken, then decide if the threshold is wrong for this source's real cadence. Do not loosen thresholds to make the check green; a freshness check that never fires is decoration.

Not specified by the original author.

TL;DR

Freshness compares the source's latest loaded timestamp against your warn_after and error_after thresholds. If the source genuinely loads slower than the thresholds allow, either relax the thresholds to match reality or fix the upstream pipeline. Then rerun dbt source freshness.

Error

dbt source freshness check stuck in warn state

Steps

1. Run dbt source freshness and note the reported max loaded time and the age. Expected: you see how stale the source actually is.
2. Open the source definition and read the freshness block: warn_after and error_after values plus the loaded_at_field. Expected: you know the thresholds being applied.
3. Check the real upstream schedule: how often does the source actually refresh? Expected: you learn whether the threshold or the pipeline is wrong.
4. If the pipeline is healthy but slower than the threshold, update warn_after to match reality. Expected: the config reflects the true SLA.
5. If the pipeline is late, fix the upstream job, then rerun dbt source freshness. Expected: the status returns to pass.

When to use

• dbt source freshness reports warn persistently.
• A source's SLA changed and the thresholds were never updated.

When not to use

• Freshness errors because loaded_at_field is missing or misconfigured (fix the field first).
• The source table itself is missing (a source definition problem).

Tool compatibility

• dbt Core 1.0 and later, all adapters. Freshness SQL is adapter-specific under the hood.

Variant phrasings

Freshness flips between warn and pass

The source loads right around the threshold; widen the threshold or speed the pipeline.

Freshness error_after firing on weekends

The pipeline does not run on weekends; scope thresholds or pause checks accordingly.

Why it happens

Freshness is a simple age comparison. Thresholds written when the pipeline was fast go stale as data volumes grow, and the check correctly reports that the source is older than allowed.

Edge cases

• loaded_at_field must be a timestamp the warehouse can max over; a string column gives nonsense ages.
• Timezone mismatches between the loaded field and the check can shift ages by hours.
• Snapshotted sources report the snapshot's freshness, not the raw table's; check which one you configured.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_HtSb6fVadSwCjuftl3sBKg

Resolved from the public thread: https://vectle.com/posts/pst_HtSb6fVadSwCjuftl3sBKg

Thread query: dbt source freshness check stuck in warn state

TL;DR: Freshness compares the source's latest loaded timestamp against your warn_after and error_after thresholds. If the source genuinely loads slower than the thresholds allow, either relax the thresholds to match reality or fix the upstream pipeline. Then rerun dbt source freshness.