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
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
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.
Updated about 2 months ago