Skills 101: erwin | How Cloud-Based Modeling Fits Alongside erwin DM

If you’re currently using erwin Data Modeler, you’ve likely started hearing more about Quest Data Modeler and how it fits alongside existing modeling workflows. In this session, we’ll walk through the practical differences between the traditional desktop experience and the newer cloud-native platform, focusing on how existing erwin users can evaluate where each approach fits within their environment. Through a live demo, we’ll explore how models, standards, governance processes, and team workflows translate between both platforms, along with where newer cloud-based capabilities can simplify collaboration and model management.

We’ll explore:
• Core workflow differences between erwin Data Modeler and Quest Data Modeler
• The browser-based modeling experience, including real-time collaboration and AI-assisted model generation
• How existing models, naming standards, and governance workflows carry forward in hybrid environments
• Using the centralized Mart repository for check-in/check-out, version history, and multi-user conflict resolution
• Which modeling workflows make the most sense for desktop, cloud, or hybrid coexistence
• Integrating with platforms like dbt, Git, Databricks, Fabric, and more

Whether you're evaluating cloud-based modeling for the first time or trying to understand how it complements your current erwin environment, this session is designed to provide a practical, technical walkthrough focused on real modeling workflows and operational considerations.