Managing Static Data in dbt: Loading CSVs with Seeds

Managing small, lookup-style datasets like country codes or regional targets shouldn't require complex data pipelines. This text-based course teaches you how to leverage dbt seeds to upload and version-control CSV data directly within your data warehouse projects. You will learn how to integrate static files into your modern data stack, ensuring consistency across your analytics engineering models. What you'll learn: - Understand the role and best practices of dbt seeds in data modeling - Configure and format CSV files for seamless loading into your warehouse - Write select statements that reference seed data using the ref function - Apply schema tests and documentation to your static seed files - Implement modern data version-control principles for lookup tables Starting with fundamental definitions of seeds and configuration basics, you will progress to building clean, reusable references inside your SQL models, wrapping up with industry-standard testing practices. This course is designed for beginner analytics engineers and data analysts who want to organize static data without relying on external ingestion tools. No advanced database administration skills are required.