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Documentation Index

Fetch the complete documentation index at: https://docs.elementary-data.com/llms.txt

Use this file to discover all available pages before exploring further.

Cloud tests run in Elementary Cloud, outside your dbt pipeline. They don’t require configuration in your dbt project — Elementary activates them automatically once your data warehouse is connected. There are three Cloud tests:
TestWhat it detects
Cloud freshness testTables that haven’t been updated within their expected window
Cloud volume testUnexpected drops or spikes in row count
Data contract testSchema mismatches against a defined contract — column names, types, nullability
Cloud tests use warehouse metadata only — not the data itself — so they add no compute cost and run continuously without pipeline dependencies.

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Adding cloud tests

Cloud freshness and volume tests are activated automatically on all your sources. You can add additional tests from the UI or via the Test Recommendation Agent:
  1. Navigate to the Test Configuration page, or select assets in the Catalog
  2. Click Add Test and choose a Table Test
  3. Select one or more tables to test
  4. Filter on Elementary Cloud and choose your test — freshness, volume, or data contract
  5. Configure the test parameters and add metadata if needed
  6. Submit — no PR required

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Editing cloud tests

Open any test result and use Anomaly settings to tune the detection. For freshness and volume tests, you can:
  • Adjust sensitivity (Low / Medium / High) to reduce noise or increase detection precision
  • Switch between automatic (ML-based) and manual (SLA threshold) modes for freshness
  • Change the detection period — the window in which the monitor looks for anomalies
  • Set anomaly direction — fail on drops, spikes, or both

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Simulate configuration

After changing any setting, use Simulate Configuration to preview how the change would have affected recent results before saving.

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Exclude time ranges

Training periods sometimes include one-time anomalies — a manual script run, a known incident — that skew the model and cause NO_DATA results. You can exclude specific time ranges so the model learns from clean data only.
  1. Click Exclude in the top-right of the chart, or go to the Test Configurations tab
  2. Mark the section of the chart you want to exclude
  3. Confirm — the excluded range appears under Excluded time ranges and can be edited or removed
After excluding, use Simulate Configuration to verify the impact.

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Removing cloud tests

  • From the Test Configuration page: select one or more tests and use the delete option at the bottom
  • From a test result: click ... → Delete test

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Supported settings

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All cloud tests

  • Severity - Should a failure be considered a warning or a failure. Default is warning.
  • Test metadata - Add metadata such as tags and owner to the test.

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Volume test

  • Anomaly Direction - Whether you want the monitor to fail on anomalous drops, spikes, or both. Default is both.
  • Sensitivity - You can set the monitor’s sensitivity levels to Low, Medium, or High. In the future, we plan to allow for more nuanced adjustments to this parameter. You can use the Simulate Configuration button for testing how the change will affect the monitor.
  • Detection Period - The period in which the monitor look for anomalies. Default is the last 2 days.

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Freshness test

You can choose between 2 detection methods for the Freshness monitor- Automatic and Manual.
  • Automatic - Elementary uses machine learning models to detect anomalies in the data freshness. This is the default setting. You can change the sensitivity level to Low, Medium, or High. For each level, you will see a simulation of the change impact on the latest result, and you can use theSimulate Configuration button to examine the change impact.
  • Manual - You can set the SLA breach threshold for the freshness monitor manually. This is useful for assets that are updated regularly at the same time every day, hour or week.
Freshness monitor configuration

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Result feedback

Use the Result feedback button on any test result to mark it as a true or false positive. Feedback improves the accuracy of the ML models over time. For false positives, select a reason:
  • Insignificant change — the anomaly isn’t impactful enough to act on; consider relaxing sensitivity
  • Expected outlier — the value should be within the expected range; triggers model retraining
  • Business anomaly — expected due to an intentional change or event; excludes the result from training
  • Not an interesting table — stop monitoring this table; deletes the monitor
  • Other — add a comment to describe the case

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Metrics

Collect and monitor custom metrics using the elementary.collect_metrics dbt test. Metrics are synced to Elementary Cloud and can have anomaly detection applied automatically.
For dbt tests and custom SQL tests, see the dbt Package and Tests tab.