What is Data Validation Rules?

Success metrics include system uptime, model performance stability, deployment velocity, and operational cost efficiency.

Define rules across three categories with increasing specificity: schema rules (data types, required columns, value formats: 'age must be integer, email must match regex pattern'), statistical rules (acceptable ranges derived from training data analysis: 'transaction_amount between 0.01 and 50000, 95th percentile below 5000'), and business rules (domain-specific constraints from subject matter experts: 'shipping_country must be in our active markets list, order_date cannot be in the future'). Store rules as configuration files (YAML or JSON) version-controlled alongside pipeline code, enabling rule changes without code modifications. Use Great Expectations or Pandera as rule engines. Review and update rules quarterly by comparing against recent production data distributions, adding rules for new failure modes discovered through incident postmortems.

Apply different strictness levels by rule category: hard constraints (schema violations, business logic impossibilities like negative quantities) should reject data immediately with zero tolerance, these catch genuine errors. Soft constraints (statistical range checks, distribution expectations) should use tiered responses: log and flag data within 1-2 standard deviations outside expected ranges, alert on data 2-3 standard deviations out, and reject only data beyond 3+ standard deviations. Set initial rule thresholds using the 99.5th percentile of training data distributions rather than absolute min/max, which are often outliers. Monitor false rejection rates (legitimate data incorrectly rejected) targeting below 0.1% of total data volume. Review rejected records weekly to identify overly aggressive rules needing relaxation or legitimate new data patterns requiring rule updates.

Define rules across three categories with increasing specificity: schema rules (data types, required columns, value formats: 'age must be integer, email must match regex pattern'), statistical rules (acceptable ranges derived from training data analysis: 'transaction_amount between 0.01 and 50000, 95th percentile below 5000'), and business rules (domain-specific constraints from subject matter experts: 'shipping_country must be in our active markets list, order_date cannot be in the future'). Store rules as configuration files (YAML or JSON) version-controlled alongside pipeline code, enabling rule changes without code modifications. Use Great Expectations or Pandera as rule engines. Review and update rules quarterly by comparing against recent production data distributions, adding rules for new failure modes discovered through incident postmortems.

Apply different strictness levels by rule category: hard constraints (schema violations, business logic impossibilities like negative quantities) should reject data immediately with zero tolerance, these catch genuine errors. Soft constraints (statistical range checks, distribution expectations) should use tiered responses: log and flag data within 1-2 standard deviations outside expected ranges, alert on data 2-3 standard deviations out, and reject only data beyond 3+ standard deviations. Set initial rule thresholds using the 99.5th percentile of training data distributions rather than absolute min/max, which are often outliers. Monitor false rejection rates (legitimate data incorrectly rejected) targeting below 0.1% of total data volume. Review rejected records weekly to identify overly aggressive rules needing relaxation or legitimate new data patterns requiring rule updates.

Define rules across three categories with increasing specificity: schema rules (data types, required columns, value formats: 'age must be integer, email must match regex pattern'), statistical rules (acceptable ranges derived from training data analysis: 'transaction_amount between 0.01 and 50000, 95th percentile below 5000'), and business rules (domain-specific constraints from subject matter experts: 'shipping_country must be in our active markets list, order_date cannot be in the future'). Store rules as configuration files (YAML or JSON) version-controlled alongside pipeline code, enabling rule changes without code modifications. Use Great Expectations or Pandera as rule engines. Review and update rules quarterly by comparing against recent production data distributions, adding rules for new failure modes discovered through incident postmortems.

Apply different strictness levels by rule category: hard constraints (schema violations, business logic impossibilities like negative quantities) should reject data immediately with zero tolerance, these catch genuine errors. Soft constraints (statistical range checks, distribution expectations) should use tiered responses: log and flag data within 1-2 standard deviations outside expected ranges, alert on data 2-3 standard deviations out, and reject only data beyond 3+ standard deviations. Set initial rule thresholds using the 99.5th percentile of training data distributions rather than absolute min/max, which are often outliers. Monitor false rejection rates (legitimate data incorrectly rejected) targeting below 0.1% of total data volume. Review rejected records weekly to identify overly aggressive rules needing relaxation or legitimate new data patterns requiring rule updates.