Views, Models, Fight: A Comparison Between dbt and Materialized Lake Views

Modern lakehouse platforms offer multiple ways to transform and serve data, but choosing the right approach isn't always straightforward. While dbt and Materialized Lake Views can both produce analytics-ready datasets, they address different challenges and excel in different scenarios. This session provides a comparison of both solutions, focusing on architecture, development workflows, governance, and operational trade-offs.

We'll cover:
dbt: How dbt applies software engineering practices to data transformations through modular SQL models, testing, documentation, lineage, and CI/CD integration.

Materialized Lake Views: How native materialized views simplify data serving with managed refreshes and optimized query performance within Microsoft Fabric.

Architecture & Trade-offs: A side-by-side comparison of maintainability, performance, governance, operational complexity, and scalability.

Live Demo: Building the same transformation using both dbt and Materialized Lake Views to highlight their differences in practice.