Data Transformation and Analytics Engineering with dbt

Why should you attend?

This course offers a comprehensive exploration of data transformation and analytics engineering using dbt, focusing on bridging the gap between data engineering and analytics. Participants will delve into the analytics engineering workflow, understanding dbt's pivotal role in modern data practices. The course provides insights into choosing between dbt Core and dbt Cloud, equipping learners with the knowledge to select the best approach for their needs. Advanced model development is a key component, where learners will build modular SQL transformations and implement effective model hierarchies. The course emphasizes optimizing model dependencies and includes hands-on exercises to refactor existing SQL into structured dbt models. Materializations are explored in depth, covering trade-offs, incremental models, performance optimization techniques, and ephemeral models. The curriculum also covers Jinja templating for dynamic SQL generation, custom macro development for standardized transformations, and package management strategies in both dbt Core and Cloud environments. Participants will learn documentation best practices to create comprehensive model documentation and implement data dictionaries. Advanced testing strategies are introduced to build robust testing frameworks and ensure data quality. Environment management is addressed with a focus on CI/CD implementation and managing configurations across different environments. Deployment strategies include job scheduling options, integrating dbt with Airflow, and handling external system dependencies. Finally, the course explores performance optimization techniques to identify bottlenecks and optimize queries. Learners will work with sources and seeds for reference data management while defining exposures and metrics for downstream consumers. Project governance is emphasized through style guides, code review practices, collaboration features, and debugging complex issues.