When transitioning from a monolith to a modular monolith or microservices architecture, breaking apart the codebase is challenging, but handling data is even harder. The data model often determines how well the new architecture performs, scales, and evolves. This session explores the essential considerations for data modeling in distributed systems, focusing on the trade-offs between distributed and universal data models.

Attendees will learn how data ownership, consistency, and communication patterns shape the boundaries between services, and how the wrong data model can lead to coupling, duplication, or performance bottlenecks. Through practical examples, the talk highlights how to align data models with architectural goals to achieve scalability, flexibility, and clarity across teams.

What You Will Learn

• The key differences between distributed and universal data models

• How to design data models that align with modular and microservice architectures

• Common pitfalls and strategies for managing data ownership and consistency

Who Should Attend

Software architects, data engineers, and developers involved in designing or refactoring distributed systems who want to make informed data modeling decisions for sustainable architecture.