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:

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, and "unknown unknowns" in 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. Monitors: SQL Rules and Comparisons 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

Additional Monitors

Some additional monitors exist, but they will soon be combined with the ones listed above. These include:

Referential Integrity Rules: these will be made available as a templated validation (SQL Rule).
Cardinality Rules: these will be made available as a templated validation (SQL Rule).
JSON Schema Change: these will be made available as a Metric.
Volume Rules: these will be combined with automated Volume monitoring, so users can easily toggle between automated and manual thresholds.

In most cases, users can choose between automated or manual thresholds. For more information on how our machine learning driven detection works, please see our Anomaly Detection section.