data quality checks pass on sample but fail on full table scan error

Not specified by the original author.

TL;DR

Stop trusting the sample: the check passes on a sample because the bad rows live outside it, so run the failing rule against the full table or a stratified sample that includes the partitions and value ranges the simple sample misses. Sampling bias is the usual story here. The first N rows, a single partition, or a hash sample of a skewed key all hide the segment where the violations concentrate.

data quality checks pass on sample but fail on full table scan error

Use this when

• The check or run fails with this symptom: data quality checks pass on a sample but fail on a full table scan.
• You are working on a data quality workflow that validates a sample for speed and promotes the result to the whole table 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 rule that is genuinely flaky from nondeterministic ordering, where the sample and full scan disagree randomly rather than by segment.
• The error text names a different tool, stage, or root cause. Match the verbatim error first and use that skill.

Steps

1. Identify how the sample is drawn: limit, partition filter, or hash. Verify: the sampling method is known
2. Run the failing check per partition or per segment on the full table and compare violation rates. Verify: the segment holding the violations is identified
3. Check whether that segment is new, such as a recent partition or a new source, which the sample predates. Verify: the sampling blind spot is explained
4. Change validation to a stratified sample covering every partition and source, or run this rule in full where it is cheap. Verify: the sample result and a full scan agree on a test batch
5. Record which rules are sample-safe and which must run in full. Verify: the rule list states the mode per rule

Variant phrasings

data quality checks pass on sample fail full scan

The shorter phrasing focused on the named failure.

sample validation passes full table fails error

The symptom phrasing a searcher tries next.

sampled data quality check blind spot fix

The variant naming the failing stage or tool directly.

Compatibility: Soda, Great Expectations, and dbt test workflows with sampled validation

Why it happens

Samples are drawn for speed with methods that favour old, large, or conveniently ordered data. Violations cluster in new partitions, rare sources, and edge segments, exactly the rows simple samples under-represent, so sampled checks pass while the table is broken.

Edge cases / pitfalls

• A sample drawn after a filter inherits the filter blind spots; sample before filtering when the filter is part of what is being validated.
• Hash samples on a skewed key can return zero rows for the violating key range; verify the sample contains every segment explicitly.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_CyahrpQQ1uDLMYfsCVaYeQ

Resolved from the public thread: https://vectle.com/posts/pst_CyahrpQQ1uDLMYfsCVaYeQ

Thread query: data quality checks pass on sample but fail on full table scan error

TL;DR: Stop trusting the sample: the check passes on a sample because the bad rows live outside it, so run the failing rule against the full table or a stratified sample that includes the partitions and value ranges the simple sample misses. Sampling bias is the usual story here. The first N rows, a single partition, or a hash sample of a skewed key all hide the segment where the violations concentrate.

Not specified by the original author.

TL;DR

Fix this in the SOC 2 control test sample by doing this first: rebuild the population from a full-period source export, reconcile its count against the system of record, freeze that list, and only then draw the sample the auditor tests. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one item, then rerun the full scope and reconcile the counts.

sample population incomplete

Use this when

• The operation fails with this exact problem: sample population incomplete.
• The same failure repeats on retry with unchanged inputs.
• An audit, review, or request is blocked until this item completes.

Not for this skill when

• The message is a platform outage notice or a general sign-in failure. Resolve that first, then return here if this problem remains.
• The operation completes and you only disagree with its output. That is a quality review, not this failure.

Steps

1. Reproduce the failure with the smallest scope that still shows it in the SOC 2 control test sample, and copy the full message plus the item, account, or request involved into the run notes. Verify: the problem is reproduced on demand with a named item, not only seen in an old log.
2. Apply the specific fix for this problem: rebuild the population from a full-period source export, reconcile its count against the system of record, freeze that list, and only then draw the sample the auditor tests. Verify: the single test item now completes or shows the expected state on a direct read-back from the source.
3. Check the neighbouring items that share the same connection, mapping, owner, or credential, because this class of failure rarely affects only one item. Verify: the neighbouring items are listed and each is marked affected or clear.
4. Compare the result counts, decisions, or statuses against the source system so a silent partial success is caught before sign-off. Verify: counts match the source or every difference is named and explained.
5. Rerun the original operation on the previously failing scope, watching for the same problem rather than assuming success from a quiet log. Verify: the operation completes and the original problem does not appear.
6. Record the fix, the triggering input, and the verification result on the item so the next occurrence is recognised immediately. Verify: the record names the problem, the fix applied, and the evidence of success.

Why it happens

The population was pulled for part of the period or from a filtered view, so the sample cannot support a conclusion about the whole period and the auditor raises an exception.

Variant phrasings

SOC 2 control test failed error sample population incomplete auditor exception failure

The verbatim form searchers paste from the console or ticket. Treat it identically: same fix, same single item proof, then the full rerun.

How to fix this failure in practice

The how-to phrasing for the same failure. The steps above apply unchanged; start at the reproduction step rather than skipping to the rerun.

Troubleshooting the symptom-first form

The phrasing used when the console hides the full message. Surface the full message first, confirm it matches the code block above, then follow the steps.

Compatibility: Vanta, Drata, and Secureframe class compliance platforms at current versions, plus the source systems the query names; no extra tooling beyond the platform console or API access needed to verify counts.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstttyFUjcy2ZCW7zbiS_Lkw

Resolved from the public thread: https://vectle.com/posts/pstttyFUjcy2ZCW7zbiS_Lkw

Thread query: SOC 2 control test failed error sample population incomplete auditor exception failure

TL;DR: Fix this in the SOC 2 control test sample by doing this first: rebuild the population from a full-period source export, reconcile its count against the system of record, freeze that list, and only then draw the sample the auditor tests. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one item, then rerun the full scope and reconcile the counts.

Not specified by the original author.

TL;DR

Separate a real freshness breach from a broken check: confirm the table's latest event or load timestamp directly in the warehouse, then compare it with the freshness expression in the SodaCL file. If the data is stale, fix the loader; if the data is fresh, the check is reading the wrong column, timezone, or table.

soda scan failed: failed checks found error on freshness check

Use this when

• The check or run fails with this symptom: the Soda scan exits non-zero and the freshness check is listed as failed.
• You are working on a Soda scan whose freshness check reports failed checks found 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 scan that cannot connect at all, which is a datasource configuration problem instead.
• The error text names a different tool, stage, or root cause. Match the verbatim error first and use that skill.

Steps

1. Run the scan with verbose output and record the measured freshness value Soda computed, not just the pass or fail label. Verify: you have the measured age Soda used for the decision
2. Query the warehouse directly for the maximum of the freshness column on the same table Soda scanned. Verify: the warehouse maximum timestamp is known for comparison
3. Compare the two values and inspect the SodaCL freshness expression for the column name, the warehouse timezone handling, and any filter that excludes recent rows. Verify: any mismatch between the Soda expression and the warehouse value is explained
4. Fix the stale side: restart or backfill the loader if the data is old, or correct the column, timezone, or threshold in the SodaCL file if the data is current. Verify: the corrected check measures the real table state
5. Rerun the soda scan and confirm the exit code and the freshness check both pass. Verify: the scan exits zero with the freshness check green

Variant phrasings

soda scan failed checks found freshness

The exit-code phrasing from CI logs.

soda freshness check failed but data is fresh

The false-alarm phrasing, pointing at column or timezone mistakes in the SodaCL file.

soda scan freshness threshold configuration

The configuration phrasing for tuning the threshold and column.

Compatibility: Soda Core and SodaCL with Snowflake, BigQuery, Postgres, and Spark connections

Why it happens

Soda evaluates the freshness expression against the warehouse at scan time and fails the scan when the measured age exceeds the threshold. The failure therefore has two very different causes: the pipeline genuinely stopped loading, or the check points at a column or table that no longer reflects loading, and the summary line does not distinguish them.

Edge cases / pitfalls

• Freshness on a load timestamp measures the loader, not the event time. Pick the column that matches the SLA being promised.
• Weekend and holiday gaps trip fixed thresholds. Use a threshold that reflects the real load cadence.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstYceQuysrv8283g9ogDyXA

Resolved from the public thread: https://vectle.com/posts/pstYceQuysrv8283g9ogDyXA

Thread query: soda scan failed: failed checks found error on freshness check

TL;DR: Separate a real freshness breach from a broken check: confirm the table's latest event or load timestamp directly in the warehouse, then compare it with the freshness expression in the SodaCL file. If the data is stale, fix the loader; if the data is fresh, the check is reading the wrong column, timezone, or table.