ELEMENTARY CLOUD 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).

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How it works?

The monitors collect metadata, and the anomaly detection model adjusts based on updates frequency, seasonality and trends. As soon as you connect Elementary Cloud Platform to your data warehouse, a backfill process will begin to collect historical metadata. Within an average of a few hours, your automated monitors will be operational. You can fine tune the configuration and provide feedback to adjust the detection to your needs.
As views are stateless, automated volume and freshness monitors only apply on tables.

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Automated Monitors

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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Alerts on Failures

🚧 Under construction 🚧