Automated anomaly detection monitors are configured on all of your sources by default. Elementary also allows you to add aditional monitors, and to edit or remove existing ones.

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Adding anomaly detection monitors

Automated anomaly detection monitors (Cloud tests) can be added directly through the UI, just like other data tests. Unlike dbt-based tests, these monitors are not part of your dbt project code, so they’re added immediately — with no need to create or approve a pull request.

To easily add a new automated monitor, follow these steps:

  • Navigate to the Test Configuration page, or select your relevant assets in the Catalog
  • Click ‘Add Test’, and choose a ‘Table Test’
  • If not selected earlier, choose one or more tables you would like to test
  • Filter on Elementary Cloud, and choose your preferred test - Volume or freshness.
  • Set up the test configurations, and add metadata if needed. Learn more about all supported settings here.
  • Review and submit your test. No PR needed - the test is set up.

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Editing anomaly detection monitors

You can change the default settings and finetune the monitors to your needs using the Anomaly settings on each test.

In general, users will rely on the automated machine learning model anomaly settings. However, in some cases, an anomaly in the data is not relevant to your business. For this cases, the custom settings are useful.

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Settings simulator

For some supported settings, Elementary offers a simulation of the change impact on latest results. You can use the Simulate Configuration button after the change and before saving.

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Remove anomaly detection monitors

There are two ways to delete monitors from the UI.

  • Test configuration page - Choose one or more tests, and an option to delete them will be available at the bottom of the page.
  • Test results page - Press the ... button on the top right of the test result and then Delete test.

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Supported settings

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All monitors

  • 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 monitor

  • 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 monitor

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.