https://community.databricks.com/t5/community-articles/databricks-lakehouse-industry-data-models-what-data-engineers/m-p/167872#M1540 <P>I recently explored the Lakehouse Industry Data Models repository published under Databricks Industry Solutions on GitHub.</P><P>The scale of the repository is impressive:</P><P>• 40 industries<BR />• 80 models across ECM and MVM variants<BR />• More than 23,000 tables and data products<BR />• More than 156,000 foreign-key relationships<BR />• More than 11,000 metric views</P><P>Each industry provides two model options:</P><P>• Expanded Coverage Model for broader domain coverage<BR />• Minimum Viable Model for a smaller, implementation-focused starting point</P><P>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.</P><P>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.</P><P>My main takeaway is that these models are most valuable as governed starting points rather than final enterprise designs.</P><P>A team could use them to accelerate:</P><P>• Domain discovery<BR />• Data-modeling workshops<BR />• Data-product identification<BR />• Source-to-target mapping<BR />• Metric-view planning<BR />• Governance and metadata discussions<BR />• AI-assisted architecture experiments</P><P>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.</P><P>GitHub repository:<BR /><A href="https://github.com/databricks-industry-solutions/lakehouse-industry-data-models" target="_blank">https://github.com/databricks-industry-solutions/lakehouse-industry-data-models</A></P><P>My detailed review:<BR /><A href="https://dataengineeringcopilot.com/blog/databricks-industry-data-models-ai-assisted-architecture" target="_blank">https://dataengineeringcopilot.com/blog/databricks-industry-data-models-ai-assisted-architecture</A></P><P>I would be interested to know whether others are using these models for architecture discovery, prototypes, or production planning.</P><P>#Databricks #DataEngineering #Lakehouse #DataModeling #UnityCatalog #MetadataManagement</P> Tue, 08 Sep 2026 05:37:56 GMT AmitDECopilot 2026-09-08T05:37:56Z https://community.databricks.com/t5/community-articles/databricks-lakehouse-industry-data-models-what-data-engineers/m-p/167872#M1540 <P>I recently explored the Lakehouse Industry Data Models repository published under Databricks Industry Solutions on GitHub.</P><P>The scale of the repository is impressive:</P><P>• 40 industries<BR />• 80 models across ECM and MVM variants<BR />• More than 23,000 tables and data products<BR />• More than 156,000 foreign-key relationships<BR />• More than 11,000 metric views</P><P>Each industry provides two model options:</P><P>• Expanded Coverage Model for broader domain coverage<BR />• Minimum Viable Model for a smaller, implementation-focused starting point</P><P>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.</P><P>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.</P><P>My main takeaway is that these models are most valuable as governed starting points rather than final enterprise designs.</P><P>A team could use them to accelerate:</P><P>• Domain discovery<BR />• Data-modeling workshops<BR />• Data-product identification<BR />• Source-to-target mapping<BR />• Metric-view planning<BR />• Governance and metadata discussions<BR />• AI-assisted architecture experiments</P><P>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.</P><P>GitHub repository:<BR /><A href="https://github.com/databricks-industry-solutions/lakehouse-industry-data-models" target="_blank">https://github.com/databricks-industry-solutions/lakehouse-industry-data-models</A></P><P>My detailed review:<BR /><A href="https://dataengineeringcopilot.com/blog/databricks-industry-data-models-ai-assisted-architecture" target="_blank">https://dataengineeringcopilot.com/blog/databricks-industry-data-models-ai-assisted-architecture</A></P><P>I would be interested to know whether others are using these models for architecture discovery, prototypes, or production planning.</P><P>#Databricks #DataEngineering #Lakehouse #DataModeling #UnityCatalog #MetadataManagement</P> Tue, 08 Sep 2026 05:37:56 GMT https://community.databricks.com/t5/community-articles/databricks-lakehouse-industry-data-models-what-data-engineers/m-p/167872#M1540 AmitDECopilot 2026-09-08T05:37:56Z https://community.databricks.com/t5/community-articles/databricks-lakehouse-industry-data-models-what-data-engineers/m-p/167876#M1541 <P>Thank you Amit for shraring.</P> Tue, 08 Sep 2026 05:41:32 GMT https://community.databricks.com/t5/community-articles/databricks-lakehouse-industry-data-models-what-data-engineers/m-p/167876#M1541 Satyasai 2026-09-08T05:41:32Z