Monitors Overview

We believe that effective monitoring solutions must minimize operational and compute costs associated with creating monitors, while maximizing the number of relevant issues detected. We accomplish this by deploying 3 types of monitors:

  • Full coverage ML-driven detection
  • Opt-in coverage ML-driven detection
  • Custom Rule-based detection

Monitor type

Issues detected

How to enable

Automatic ML-driven detection

Freshness: MC monitors how often each table in your environment is updated, and alerts you if there is a delay

Volume: MC monitors how much data is added/ removed/ updated for each table with each update, and alerts you if tables grow or shrink unexpectedly

Schema: MC monitors all schema changes in your environment, and alerts you of added, removed or updated fields and deleted tables

No action needed. These are automatically enabled for all tables which MC has access to. For views, only schema detection will apply.

Opt-in ML-driven detection

Field Health: MC calculates and monitors multiple metrics and statistics (see full list here), and alerts you if there are substantial changes in those metrics and statistics

Dimension Tracking: MC monitors the frequency of field values (best for low-cardinality fields) and alerts you of unexpected changes in the distribution

JSON Schema: MC monitors fields with nested JSON values for changes in the structure, and alerts you if that structure changes

Navigate to the Monitors tab and select the type of monitor you want to enable. Follow the flow to opt-in your important data tables, views, or specific columns.

Custom Rule-based detection

SQL Rules: MC makes it easy for data teams to define custom rules using SQL statements to check for specific conditions, and alerts you when those conditions are breached

Navigate to the Monitors tab and select Rules.


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