Adopting dbt™ marks a significant leap towards governed data transformations.
But with every game-changer, big questions arise: Where do data transformations end? Should they touch the BI layer? What roles do data engineers, analytics engineers, and business analysts play in data modeling? And, is centralizing metrics truly beneficial? Spoiler: It’s about finding the balance between self-serve analytics and central governance.
I’m so grateful for the opportunity to share my best practices for scaling dbt at dbt Labs’ Coalesce Conference 2024 just a month ago. My co-panelists and I tried to offer an alternative way to handle transformations and metrics without compromising analyst freedom or causing data team burnout.
Tune in to me, Mark Nelson (former CEO and president of Tableau and now a Venture Partner at Madrona), Silja Märdla (Senior Analytics Engineer at Bolt), and Patrick Vinton (CTO at Analytics8) to learn how to manage business logic as your data operations grow and build a robust metrics layer in dbt that ensures trustworthy AI-driven analytics.
