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Profiler Configuration

Profiler Configuration sets the platform-wide defaults the profiler applies before any individual profiler workflow runs:
  • Which metrics it computes for each column data type
  • Whether it stores or reads sample data
These defaults apply to every profiler workflow unless a workflow overrides them. For more information about running the profiler itself, see Profiler Workflow. Purpose and Benefits
  • Purpose: Set which profiler metrics run by default, per data type, and whether sample data is stored or read.
  • Benefits: Gives every new profiler workflow across every team a consistent, already-tuned baseline instead of reconfiguring the same metrics from scratch.

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How to Access

Navigate to Settings > Preferences > Profiler Configuration. Profiler Configuration page

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Metric Configuration

Metric Configuration lets you disable metrics per data type, which is useful when:
  • A metric doesn’t make sense for that type, such as computing a mean on a string column
  • You want to keep profiling fast on very wide tables by skipping metrics your team doesn’t act on
The default Metrics list doesn’t include histogram, cardinalityDistribution, or system, among others. Add them to the Metrics field on the profiler agent if you use the column distribution charts or system data manipulation language (DML) metrics. Upgrading keeps your existing sample settings as Static, but turns Randomized Sample off and removes the deprecated Compute Metrics setting. Add a row per data type to control which metrics the profiler computes for columns of that type:
  1. Click Add Field.
  2. Choose a Data Type, for example INT, STRING, or TIMESTAMP. Each data type can only be configured once.
  3. Choose which Metric Type(s) to disable for that data type, or leave it on All to disable every metric for that type.
  4. Use the Disable toggle to turn the whole row off without deleting it.
Click the X button next to a row to remove it. Metric Configuration rows by data type

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Sample Data Ingestion Configuration

Sample data lets users preview real rows from a table directly in OpenMetadata, which is convenient for spotting data quality issues at a glance, but it also means row-level data is brought outside the source system: Sample Data Ingestion Configuration toggles Click Save to apply your changes.