🏰Data Vault

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Also known as: Hub-and-Spoke Model, Data Vault Modeling, DV2.0

A scalable data modeling method using hubs, links, and satellites to integrate and historize enterprise data for analytics.

DESCRIPTION

Data Vault is a modeling approach for analytics that separates business keys (hubs), relationships (links), and descriptive context (satellites). It emphasizes auditability, historization, and flexibility to accommodate change. Raw Vault stores source records with minimal transformation; Business Vault adds business logic. The pattern supports parallel loads, agile development, and traceability of data lineage. It is often implemented in cloud data warehouses with ELT. Data Vault is complementary to dimensional marts, which can be built downstream.

ALTERNATIVE DEFINITIONS

Wikipedia

Data vault modeling is a database modeling method that is designed to provide long-term historical storage of data coming in from multiple operational systems.

Emphasizes historical storage and multi-source integration.

Source

Data Vault Alliance

The Data Vault is a detail oriented, historical tracking and uniquely linked set of normalized tables which support one or more functional areas of a business.

Canonical definition highlighting detail, history, and linked normalization.

Source

RELATED TERMS

Hub Relationship Satellite Raw Vault Business Vault Extract-Transform-Load Data Warehouse Hubs Links Satellites Bitemporal Modeling Surrogate Key Auditability Historical Tracking

COMPETING TERMS

Kimball Methodology Third Normal Form Anchor Modeling

RELATIONS

• Broader: Vault

KNOWLEDGE AREAS

Data Warehousing & BI Data Architecture Data Modeling Data Management

TARGET AUDIENCE

Data Architect Data Engineer Analytics Engineer BI Specialist Data Modeler Chief Data Officer Data Governance Manager

EXTERNAL SOURCES

• Wikipedia - Data vault modeling
• Snowflake - Vendor guide to Data Vault
• Data Vault Alliance - Official guidance and resources
• Vertabelo - Introductory article on basics

TOP QUESTIONS

What business problems does DataVault address?

It simplifies integrating disparate sources with changing schemas while preserving history and auditability at a granular level.

Category: Value

How do Hubs, Links, and Satellites work in DataVault?

Hubs store business keys, links model relationships, and satellites hold descriptive history tied to hubs or links.

Category: Concepts

When should I choose DataVault over dimensional modeling?

Choose it when frequent source change, auditability, and scalable ingestion outweigh the complexity of more joins.

Category: Guidance

What is a Raw Vault versus a Business Vault?

Raw Vault stores source-accurate data; Business Vault contains derived, business-aligned entities and calculated attributes.

Category: Modeling

How does DataVault support bitemporality?

Satellites can track load (transaction) and valid times, enabling bitemporal queries and corrections.

Category: Advanced

What are common mistakes with DataVault?

Misidentifying business keys, overloading satellites, and skipping hash-based keys can harm performance and lineage.

Category: Mistakes

How does DataVault feed analytic layers?

Downstream ELT materializes star schemas or wide tables from the vault to simplify consumption.

Category: Architecture

What performance practices are important in DataVault?

Hash keys, columnar storage, compression, and partitioning/cluster keys alleviate join costs.

Category: Performance

How does governance benefit from DataVault?

Granular lineage, timestamps, and soft business rules make audits and traceability easier.

Category: Governance