Architecting the Future of Enterprise Data

ACHIEVING QUALITY AND COMPLIANCE A Data Vault + dbt approach fundamentally enhances data quality and compliance.

Data Quality Assurance • Source-to-Vault Validation:

• Monitoring: dbt and Data Vault timestamps enable monitoring of data freshness and volume anomalies, proactively catching issues. • Design Advantage:

dbt tests immediately flag issues (e.g., unique key violations, nulls) as data enters the Vault from staging, leveraging Data Vault’s insert-only nature. • Semantic Testing: In Business Vaults/Marts, unit tests validate business logic application, ensuring semantic accuracy and that transformed data aligns with business definitions.

Data Vault’s separation of raw data capture from business interpretation preserves original data, reducing irreversible quality errors.

Compliance and Governance • Historical Retention: Data Vault’s inherent historical storage in Satellites ensures compliance with data retention regulations. • Transparency & Explainability: Data Vault’s historical data retention and source tracking, combined with dbt’s automated lineage graphs, versioning, and comprehensive documentation, provide clear and robust

• Access Control & Data Privacy: In dbt, you can manage which schemas users have access to. Sensitive data might be kept in certain Satellites that only a subset of analysts or applications can query. • Audit Logs & Change Management: dbt’s job logs and code changes, combined with Data Vault’s data-level audit trail, provide comprehensive traceability for compliance and debugging.

audit trails. This is vital for regulated industries and AI governance, as it allows for precise tracing of data origins and transformations.

In practice, companies that implement Data Vault on dbt often find that issues are easier to debug and trust from governance teams increases, fostering confidence in data-driven decisions.

ARCHITECTING THE FUTURE OF ENTERPRISE DATA