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Constraints that verify data values meet defined quality, format, and business standards before acceptance or processing.
DESCRIPTION
Validation rules enforce data fitness-for-purpose by checking formats, ranges, referential integrity, and business logic. They are applied at ingestion, modeling, or query time to prevent bad data from propagating. Declarative frameworks like Great Expectations and dbt tests simplify rule authoring. Rules should be versioned, monitored, and aligned with data contracts. Effective validation improves trust, reduces rework, and supports governance.
ALTERNATIVE DEFINITIONS
Great Expectations
Expectations are declarative, readable assertions about data.
Frames validation rules as human-readable assertions on datasets.
dbt Labs
Tests are assertions that validate the correctness of your models.
Positions tests as validation assertions in analytics engineering.
W3C
The Data Quality Vocabulary (DQV) is a vocabulary to express data quality information.
Provides standardized vocabulary to describe data quality, including constraints and metrics.
JSON Schema
JSON Schema is a vocabulary that allows you to annotate and validate JSON documents.
Defines schema-based validation for JSON structures.
RELATED TERMS
Data Validation Constraints Schema Enforcement Business Rules Data Contracts Data Profiling Data Quality Referential Integrity Quality Metrics
COMPETING TERMS
KNOWLEDGE AREAS
Data Quality Data Management Metadata Management Testing & Validation Data Integration & Interoperability
TARGET AUDIENCE
Data Steward Data Engineer Data Architect Analytics Engineer BI Specialist Data Analyst
EXTERNAL SOURCES
- Wikipedia - Data validation
- Great Expectations - Open-source data validation framework
- dbt Labs - Testing and validation for analytics models
- W3C - Standard vocabulary for data quality
- JSON Schema - Step-by-step JSON validation guide
TOP QUESTIONS
Why are Validation Rules critical for data products?
They prevent bad data from entering downstream pipelines, protecting analytics and machine learning from incorrect inputs.
Category: Relevancy
Where should Validation Rules be applied?
At ingestion, transformation, and consumption layers, aligned with data contracts and business requirements.
Category: Requirements
What tools help implement Validation Rules?
Great Expectations, dbt tests, JSON Schema, and database constraints are common choices.
Category: Platform
What are common mistakes with Validation Rules?
Overly strict rules that block legitimate data and rules without monitoring or version control.
Category: Mistakes
How are Validation Rules monitored?
Track rule violations, alert on thresholds, and review trends to improve rules and data quality.
Category: Observability
What’s the risk of relying solely on schema validation?
Schema checks types and structure but may miss business logic errors; combine with semantic rules.
Category: Risk
How do Validation Rules relate to data contracts?
Contracts formalize expectations; rules enforce them at runtime, ensuring producers and consumers align.
Category: Collaboration
Can Validation Rules be dynamic?
Yes, rules can evolve with seasonality, domain changes, or new business constraints.
Category: Management
How to start with Validation Rules?
Profile data, prioritize critical fields, implement baseline rules, and iterate with stakeholder feedback.
Category: Learning