ELEMENTARY CLOUD

ML-powered anomaly detection monitors automatically identify outliers and unexpected patterns in your data. These are useful to detect issues such as incomplete data, delays, a drop in a specific dimension or a spike in null values.

Elementary offers two types of monitors:

  • Automated Monitors - Out-of-the-box monitors activated automatically, that query metadata only.
  • Opt-in Monitors - Monitors that query raw data and require configuration.

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Automated monitors

Out-of-the-box ML-powered monitoring for freshness and volume issues on all production tables. The automated monitors feature provides broad coverage and detection of critical pipeline issues, without any configuration effort.

These monitors track updates to tables, and will detect data delays, incomplete updates, and significant volume changes. Additionally, there will be no increase in compute costs as the monitors leverage only warehouse metadata (e.g. information schema, query history).

Automated Freshness

Monitors updates to tables and how frequently a table is updated, and fails if there is an unexpected delay.

Automated Volume

Monitors how many rows were added or removed to a table on each update, and fails if there is an unexpected drop or spike in rows.

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Opt-in monitors

Coming soon

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How anomaly detection works?

🚧 Under construction 🚧

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Monitor test results

Each monitor returns a test result, that is one of the following four results:

  • Passed - The test passed, no anomaly was detected.
  • Warning - An anomaly was detected, and the test is configured to warn severity.
  • Fail - An anomaly was detected, and the test is configured to fail severity.
  • No data - The monitor does not have enough data or an accurate model to monitor. Reach out to our support team to fix this.
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