Glossary

Data vault

A modeling methodology that splits data into hubs, links, and satellites to make a warehouse auditable and adaptable to change.

Data vault is a data modeling methodology for the core layer of a warehouse, built around three table types: hubs, which store a business entity's unique business key; links, which record relationships or transactions between hubs; and satellites, which hold the descriptive, time-stamped attributes for a hub or link. It is designed for auditability and resilience to source-system change rather than for direct end-user querying.

Because business keys, relationships and descriptive attributes are stored in separate table types, a data vault can absorb new source systems, new attributes, or changed relationships by adding tables rather than altering existing ones, and every satellite row carries a load timestamp so the full history of every change is preserved automatically — effectively slowly changing dimension Type 2 built into the architecture. This flexibility comes at the cost of many more joins than a conventional star schema.

Data vault is typically used as a staging and history layer, with a separate dimension table-and-fact star schema or data mart built on top for reporting, since the vault's structure is optimized for loading and lineage, not for BI tool performance. It suits organizations with many changing source systems and strict audit or regulatory requirements, and is generally overkill for a small, stable set of sources.

Last reviewed September 22, 2026

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Related terms

Dimension table A table of descriptive attributes, such as customer or product details, that a fact table references for context. Surrogate key An artificial, meaningless identifier generated by the warehouse to uniquely identify a dimension or fact row. Slowly changing dimension The techniques for recording how a dimension table's attributes change over time without breaking historical reporting. Data warehouse A centralized, structured database optimized for fast historical reporting rather than day-to-day transactions. Data modeling The practice of organizing data into entities, relationships and schemas that make it accurate and efficient to query. Schema evolution The ability of a table to accommodate structural changes, like an added or renamed column, without breaking data.

Related tools

Synthetic Data Vault Open-source Python library for generating synthetic tabular, relational and time-series data from real datasets you run yourself.