Skills 101: erwin

Building robust models requires moving seamlessly between Conceptual, Logical, and Physical layers- and knowing how to maintain integrity across them. In this session, we’ll show you how to derive Logical models from Conceptual foundations, enrich them with attributes, and produce Logical/Physical models across multiple data structures. You’ll learn techniques for handling relationships, managing large models, and scaling design efforts for enterprise environments. By the end, your layered models will be as elegant and precise as a well-oiled DevOps pipeline.
Topics that will be discussed include:

• Designing Conceptual models that scale logically
• Deriving Logical models with attributes and relationships intact
• Transitioning to Physical models efficiently
• Handling multiple data structures without losing your mind

Speaker: Andy McGovern, Sr. Principal Consultant

erwin Data Modeler is getting a long-overdue refresh. While erwin Data Modeler has supported platforms like Azure, Databricks, and Snowflake for years, this session focuses on what’s changed in r16, what’s improved, and how the updated interface better aligns with modern data workflows without disrupting existing processes.

In this session, we’ll explore:

Updated UI and UX aligned to modern data modeling workflows
New architecture and its impact on performance and scalability
Key feature updates that improve day-to-day usability
DM Lite Cloud Client and expanded accessibility across environments

A practical look at how the latest updates fit into real-world data modeling workflows.

Manual reverse engineering and model comparisons don’t scale, especially across larger environments.
This session shows how to automate core modeling tasks in erwin Data Modeler Workgroup Edition so updates, comparisons, and reporting run consistently without manual effort.

What you’ll learn:
• Schedule Reverse Engineering + Complete Compare jobs
• Track changes with difference reports
• Securely store parameters and credentials
• Monitor and manage jobs across multiple models
Walk away with a repeatable, hands-off approach to keeping models aligned and up to date.

Stay for the live Q&A and bring your questions.

Managing data models across Dev, QA, and Production can quickly become fragmented without a clear, repeatable process.
In this Skills 101 session, we’ll walk through how to use erwin Data Modeler Workgroup Edition to align your modeling workflow with modern SDLC and CI/CD pipelines, helping you move models confidently from development through production.

We’ll walk through:
• Creating libraries and milestone versions to track changes and maintain traceability 
• Promoting models across Dev, QA, and Production stages with consistency 
• Pushing DDL to CI/CD repositories securely and automatically 
• Aligning modeling workflows with enterprise DevOps practices

You’ll see how to build a structured, repeatable approach that keeps models in sync with your development lifecycle and reduces friction between teams.