Intermediate dbt for Production Data Pipelines

Scaling data transformation requires more than just writing basic SQL queries. To build reliable, production-grade data pipelines, you need to implement robust testing, automation, and historical tracking. This text-based course guides you through intermediate dbt techniques to elevate your data analytics engineering. You will transition from writing simple models to configuring resilient, automated, and fully documented data workflows that scale with your organization. What you'll learn: - Configure advanced testing strategies, including custom schema tests and validation rules for sources and seeds. - Implement dbt snapshots to track historical data changes using Slowly Changing Dimensions (SCD Type 2). - Optimize pipeline execution using dbt build to streamline models, tests, and snapshot runs. - Manage static datasets effectively using dbt seeds and document data lineage for better governance. - Apply modern project structuring and modular design principles to keep your dbt project clean and maintainable. - Understand the fundamentals of continuous integration (CI) workflows for deploying dbt changes safely. You will start by reviewing essential terminology and foundational project structures before diving into step-by-step written explanations of advanced configurations, code snippets, and practical design patterns. This course is designed for data analysts, analytics engineers, and developers who have a basic grasp of SQL and fundamental dbt concepts and are ready to build production-ready pipelines. Start reading today to build cleaner, more reliable data transformation workflows.