lakehouse-monitoring-comprehensive

To set up Databricks Lakehouse Monitoring for Gold layer tables, you use the Data Quality API and Python SDK to initialize monitoring, define custom metrics, and generate output tables for dashboards. This skill guides you through the complete setup process.

You can define custom business metrics using AGGREGATE, DERIVED, and DRIFT types for data quality monitoring. These allow you to track KPIs like revenue, monitor business logic, and detect data deviations over time in your Gold layer tables.

Databricks Lakehouse Monitoring initialization failures can be troubleshot using the Data Quality API and Python SDK. This skill provides patterns to diagnose common initialization issues, verify table configurations, and resolve setup errors for Gold layer monitoring.

Yes, you can track daily revenue drift and set up alerts for significant drops by defining DRIFT metrics on tables like fact_sales_daily. The monitoring output tables capture deviations over time, enabling you to query results and trigger alerts when values fall below thresholds.

You query monitoring tables by accessing the output tables generated during Lakehouse Monitoring initialization. These tables contain data profiling results, custom metric values, and drift detection data that can be directly used to build dashboards in Databricks SQL.