Enterprises often struggle to manage data consistently across multiple systems, leading to configuration drift and regulatory risk.

This session presents a model driven data architecture where business domain model is elevated to a system agnostic, executable specification. Legend, an open source platform, enables this approach through a pure logical modeling language and extensible, declarative annotations as first class constructs, allowing teams to encode governance, security, and behavioral policies directly into the model at design time. These enriched models are compiled by a policy as code translation engine into platform specific artifacts ensuring deterministic and repeatable enforcement across technologies. The model derived design acts as an AI enabler — generating precise machine-readable semantics from a single model, reducing hallucination risk and enabling trustworthy automation.

What You Will Learn

• Logical versus physical modeling and why separation is critical for durable data assets

• How governance, security, and policies can be embedded directly into logical data models

• How compiling models into platform-specific artifacts eliminates configuration drift and improves trust

Who Should Attend

• Data architects

• Enterprise architects

• Platform engineers

• Developers working on data-intensive systems

• Technology leaders responsible for data governance and compliance