anomaly-detection

Automate anomaly detection in dbt pipelines by running the anomaly-detection workflow against your project to identify volume spikes, freshness issues, and distribution shifts. It generates anomaly YAML tests and schema validations to catch data problems.

You can monitor data anomalies in ETL workflows to detect volume spikes, data freshness issues, and distribution shifts. This applies to dbt-led analytics stacks across production ETL and data quality workflows.

Yes, you can use custom SQL and statistical checks for data quality monitoring instead of Elementary-based tests. This provides flexible coverage to tailor alerts and thresholds to your specific data stack context.

Set up alerting for data freshness issues and distribution shifts by applying anomaly detection to your dbt analytics stack. The workflow integrates with data-quality-testing blocks and exposes test CLI tools to produce alerts.

This anomaly detection approach applies to dbt-led analytics stacks. It integrates with your data stack context to tailor alerts and thresholds, working with the data-stack-context and data-quality-testing blocks.