Check Types

Weiser supports various types of data quality checks to validate different aspects of your data. Each check type serves a specific purpose and can be configured with different conditions and thresholds.

Available Check Types

Basic Checks

• Row Count - Validates the number of rows in a dataset
• Numeric - Custom numeric expressions and calculations
• Sum - Validates the sum of a numeric column
• Min - Validates the minimum value of a column
• Max - Validates the maximum value of a column
• Measure - Cube.js specific measure validation

Data Completeness Checks

• Not Empty - Validates NULL values in dimensions (count-based)
• Not Empty Percentage - Validates NULL values in dimensions (percentage-based)

Advanced Checks

• Anomaly Detection - Detects anomalies using statistical methods

Common Configuration

All checks share common configuration options:

• name: Unique identifier for the check
• dataset: Target table or SQL query
• type: The check type (see list above)
• condition: Comparison operator (gt, ge, lt, le, eq, neq, between)
• threshold: Value(s) to compare against
• dimensions: Group by columns (optional)
• filter: WHERE clause conditions (optional)
• time_dimension: Time-based aggregation (optional)

Example Configuration

checks:
  - name: orders_row_count
    dataset: orders
    type: row_count
    condition: gt
    threshold: 1000
    
  - name: revenue_sum_by_region
    dataset: sales
    type: sum
    measure: revenue
    dimensions: [region]
    condition: ge
    threshold: 50000