Modeling for success: Building data structures that last
🌐 Global friendly sessions on September 30 & October 1
Poor data modeling is one of the most common and costly mistakes teams make when implementing dbt. Quick fixes and one-off designs may seem faster in the moment, but they create hidden risks: redundant code, fragile pipelines, and data that’s hard to trust or scale. A well-designed model is the foundation for sustainable analytics.
Join experts from dbt Labs and phData as they share proven strategies for structuring models that align to business processes, reduce complexity, and keep projects future-ready. You’ll learn why dimensional modeling is the most effective approach for dbt’s batch-oriented workflows, and how intentional design pays dividends in speed, cost, and trust.
- Clarity on modeling approaches: Compare dimensional, data vault, relational, and one-big-table models, and learn when each makes sense
- Best practices in action: See examples of good and bad modeling decisions, and their impact on scalability, cost, and governance
- A blueprint for success: Learn how to translate business requirements into technical designs that evolve with your needs
- Real-world insights: Hear stories from experts who’ve guided dozens of dbt implementations—what works, what doesn’t, and why modeling right matters most
- Get your questions answered live by a dbt product expert
Meet your speakers:
Dustin Dorsey
Data Engineering Director
phData
Dakota Kelley
Principal Solutions Architect
phData
Stephen Robb
Partner Solutions Architect
dbt Labs
Host partner:
