The Field Engineering (FE) Data Modeling project (tracked under FEIP-1812) was established in October 2025 to address a critical customer gap: the lack of out-of-the-box, industry-standard data models to accelerate onboarding and migration to Databricks.
The project focuses on creating UC-ready industry data models, ETL mappings, and semantic layers that allow field teams to demonstrate how Databricks can map real customer data to industry standards.
Standardization: Ship unified data models (Bronze → Silver → Gold) for key industries to accelerate our customer's projects.
AI-Enhanced Accuracy: Leverage a "Data Intelligence" approach where well-structured models reduce token usage and increase the accuracy of AI-generated responses.
Automated Modeling (Vibe): Utilize the Vibe Data Modeling tool to automate the creation of complex data models, reducing months of work to just a few iterations.
Repeatable Demos: Integrate these models into standard field assets, including GitHub repositories, DDLs, and KPI/Metric views that can be used immediately in customer conversations.