Use case — Databricks (coming soon)
Databricks processes your data. The Data Vault structures them.
Databricks excels at large-scale processing, machine learning and exploration. But a lakehouse without a reference model produces data no one can rely on. beVault generates Data Vault structures on Delta Lake and exposes historized, traceable, quality-scored data — exactly what your AI models and reports demand.
- Data Vault structures generated on Delta Lake
- Native historization: every data state is preserved and datable
- Lineage from the source field through to the training set
- Exposure via API and MCP server for your AI agents
Challenges
The lakehouse without structure: what you end up with
Bronze/Silver/Gold flow
Layers keep multiplying, promotion rules go undocumented, and no one knows which Silver table is authoritative for which use case.
No single source of truth
Each team builds its own aggregation. Figures diverge between notebooks, and reconciliations take days.
Non-reproducible training sets
An ML model trained today can't be recreated identically six months from now. Regulatory compliance becomes a problem.
Invisible Quality
Anomalies are detected in model results, not at the source. The cost of correction is maximal.
What beVault provides
A Data Vault foundation on your lakehouse
- 01
Modeling on Delta Lake
Data Vault structures are generated directly on Delta Lake. The Bronze layer remains intact as a raw source of truth.
- 02
Historization and quality
Every record is historized with its load date and quality score. The state of the data at any past date can be reconstructed.
- 03
Governed exposure
Marts are generated with full lineage. Your data scientists access versioned and documented datasets.
- 04
Connecting to AI agents
Via the API and the MCP Server, agents can interact with the exposed metadata and data according to configured permissions and controls.
Frequently asked questions
Databricks and beVault
Does beVault replace Databricks notebooks?
No. beVault handles the structuring and governance layer. Your data scientists keep Databricks for exploration and ML.
Is it compatible with Unity Catalog?
Contact us to discuss your Unity Catalog setup and the integration scope.
Is Databricks already supported?
Not yet. Snowflake, Amazon Redshift, Microsoft SQL Server, and PostgreSQL are supported today; Databricks is coming soon. Contact us to discuss your timeline.
Can we expose data to Power BI or Tableau?
Yes. Data marts are directly exposed to BI tools via standard connectors.
Next step