Monitors Overview

Monte Carlo offers a variety of monitors so that data teams can:

Instantly deploy broad monitoring of your data pipelines, with no manual configuration. These monitors use metadata whenever possible so that compute costs are minimal.
Strategically deploy deep quality monitoring on the tables that are more important. These monitors directly query the data, to alert to shifts in the data's profile, failed validations, and more.

Monitor Categories

Monitors in Monte Carlo fall into 4 categories. In most cases, users can choose between automated or manual thresholds. For more information on our machine learning detection, please see our Anomaly Detection section.

Category	Description
Pipeline Observability	Monte Carlo learns the normal patterns of updates, size changes, and growth for any given table, and alerts if those patterns are violated. Ideal for detecting breakages and stoppages in the flow of data. For most integrations, these monitors start working immediately without user configuration and use metadata instead of querying the table. Monitors: Freshness, Volume, and Schema Change
Metrics	Monte Carlo learns the statistical profile of data within the fields of a table, and alerts if those patterns are violated. Ideal for detecting breakages, outliers, "unknown unknowns," and anomalies within specific segments of data. See all available metrics. Monitors: Metric Monitors and Dimension Tracking
Validations	Users can choose from templates or write their own SQL to check specific qualities of the data. Ideal for row-by-row validations, business-specific logic, or comparing metrics across multiple sources. See all available conditions. Monitors: Validation Monitors, SQL Rules and Comparison Rules
Performance Observability	Users can track runtime of queries and be alerted to outliers and queries that aren't scaling well. Ideal for ensuring that pipelines remain reliable and that costs aren't spiraling out of control. Monitors: Query Performance