Column Metrics & Validation

Column-level statistics, validation rules, and value monitoring

Column-level monitoring lets you collect detailed statistics and apply validation rules to individual columns. These metrics feed into anomaly detection over time, alerting you when column values deviate from historical patterns.

Column Statistics

Key	Type	Description
count	bool	Total non-null and null value counts
null_count	bool	Number of null values
count_distinct	bool	Number of unique values (cardinality)
unique_count	bool	Unique value count
size	bool	Total size in bytes of column values
min_max_mean	bool	Minimum, maximum, and mean values for numeric columns
min_max_len	bool	Minimum and maximum string length for text columns
percentile	bool	Percentile statistics (p50, p90, p95, p99) and standard deviation

objects:
  public.orders:
    columns:
      revenue:
        count: true
        min_max_mean: true
        percentile: true

      customer_id:
        count_distinct: true
        null_count: true

      description:
        min_max_len: true

Column Validation

Validation rules check column values against defined patterns or value lists. Violations are reported as anomaly events.

Regex Patterns

Use regex_match to define patterns that values should match, and regex_not_match for patterns that values should not match:

objects:
  public.users:
    columns:
      email:
        regex_match:
          - "^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\.[a-zA-Z0-9-.]+$"

      phone:
        regex_not_match:
          - "^000"        # flag placeholder numbers
          - "^555"        # flag test numbers

Value Lists

Use accepted_values to define valid values (anything else is a violation), and rejected_values to define values that should not appear:

objects:
  public.orders:
    columns:
      status:
        accepted_values:
          - pending
          - processing
          - shipped
          - delivered
          - cancelled

      source:
        rejected_values:
          - test
          - unknown
          - "null"
          - ""

Validation results include match counts, violation counts, and a valid boolean. These are tracked over time and can trigger anomaly alerts when violation rates change.

Column Wildcard

Use "*" as a column name in defaults to apply metrics to all columns across all monitored objects:

defaults:
  columns:
    "*":
      null_count: true
      count_distinct: true

objects:
  public.users: {}          # all columns get null_count + count_distinct
  public.orders:
    columns:
      revenue:
        min_max_mean: true   # adds to the inherited wildcard metrics

Complete Example

connection: MY_POSTGRES

defaults:
  metadata: true
  row_count: true
  columns:
    "*":
      null_count: true

objects:
  public.users:
    columns:
      email:
        count_distinct: true
        regex_match:
          - "^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\.[a-zA-Z0-9-.]+$"
      status:
        accepted_values:
          - active
          - inactive
          - pending
      name:
        min_max_len: true

  public.orders:
    freshness_threshold: "6h"
    freshness_column: created_at
    columns:
      total:
        min_max_mean: true
        percentile: true
      quantity:
        min_max_mean: true
      discount_code:
        count_distinct: true
        rejected_values:
          - test
          - "EXPIRED_2023"

  public.products:
    columns:
      price:
        min_max_mean: true
        percentile: true
      category:
        count_distinct: true
        accepted_values:
          - electronics
          - clothing
          - food
          - home