armor-monitor

Automate data freshness monitoring by configuring scheduled checks via the AnomalyArmor Python SDK. It supports auto-learning or explicit thresholds to verify your data tables update within expected intervals.

Schema drift detection identifies unexpected changes in data schemas to maintain data reliability. By capturing a baseline schema and monitoring for deviations, it proactively alerts you to structural changes in your data pipelines before they cause downstream issues.

Yes, you can configure data freshness checks with explicit thresholds. The monitoring setup allows choosing between auto-learning to adapt to data patterns and explicit thresholds to enforce strict update interval rules for critical tables.

To set up daily schema change monitoring, use the SDK to enable baseline capture for your target table, such as a 'customers' table. This creates a reference schema and continuously monitors for any structural deviations or drift on your chosen schedule.

Yes, the AnomalyArmor Python SDK is a required dependency for configuring schema drift alerts and freshness checks. The Skill relies on this SDK to automate the setup and management of your data monitoring configurations.

Automated data monitoring is ideal for proactive data quality assurance across critical pipelines. You should use it when you need continuous, scheduled oversight of data freshness and schema integrity to prevent undetected pipeline failures or data anomalies.