In this session, we'll dive into the critical aspects of strategic data modeling in dbt and highlight the pitfalls of neglecting proper planning. Many dbt users jump straight into building data models without considering important factors such as data source dependencies, reusability, documentation, testing, scalability, and performance. This can result in technical debt and costly mistakes that often require consultants to clean up.

Through real-world examples, we'll explore the consequences of failing to plan ahead in dbt and share practical strategies for avoiding common pitfalls. Whether you're an experienced dbt user or just starting out, this session will provide you with actionable insights to strategically plan and architect your data models in dbt for success, preventing potential headaches and the need for external consultants. Join us to learn how proper planning in dbt can save you time, effort, and resources in the long run, and ensure a smooth and efficient data engineering process.