Column-level statistics out of the box: null counts, unique values, min/max, averages, sum, and value distribution

Custom profiling rules per column for tailored analysis beyond the defaults

Profiling results directly in the catalog and used to generate data validation suggestions

Historical trend graphs showing how column-level metrics evolve across every profiling run

Schedule profiling tasks by interval or CRON expression to keep metrics continuously fresh

Granular history management: exclude individual executions or specific values from analysis without losing the underlying data

Machine learning-based dynamic thresholds that adapt to each column’s actual behavior over time

Automatic flagging and alerting when values deviate from learned patterns

Configurable alerts per profiling object, delivered by email or webhook