I recently explored the Lakehouse Industry Data Models repository published under Databricks Industry Solutions on GitHub.
The scale of the repository is impressive:
• 40 industries
• 80 models across ECM and MVM variants
• More than 23,000 tables and data products
• More than 156,000 foreign-key relationships
• More than 11,000 metric views
Each industry provides two model options:
• Expanded Coverage Model for broader domain coverage
• Minimum Viable Model for a smaller, implementation-focused starting point
The repository includes much more than entity names. Engineers can inspect model JSON, SQL schemas, relationships, metric views, ontology tags, generated documentation, and DBML diagrams.
It also provides tooling to install a selected model into Unity Catalog, populate it with referentially consistent sample data, and visually explore relationships through the model viewer.
My main takeaway is that these models are most valuable as governed starting points rather than final enterprise designs.
A team could use them to accelerate:
• Domain discovery
• Data-modeling workshops
• Data-product identification
• Source-to-target mapping
• Metric-view planning
• Governance and metadata discussions
• AI-assisted architecture experiments
The business definitions, grain, keys, regulatory requirements, and source-system realities still need validation by engineers and domain experts. AI can accelerate the initial structure, but production architecture still requires human review and contextual knowledge.
GitHub repository:
https://github.com/databricks-industry-solutions/lakehouse-industry-data-models
My detailed review:
https://dataengineeringcopilot.com/blog/databricks-industry-data-models-ai-assisted-architecture
I would be interested to know whether others are using these models for architecture discovery, prototypes, or production planning.
#Databricks #DataEngineering #Lakehouse #DataModeling #UnityCatalog #MetadataManagement
Amit Kumar Singh
Lead Data Engineer | AI-Assisted Data Engineering
