Hands-on Analytics Engineering Bootcamp With: Theory, Building a dbt Project from Scratch, and Deploying to dbt Cloud
InstructorJack Colsey
Editorial course preview
These 4 complementary views highlight concrete, legible examples from the course presentation.
A diagram illustrating a manual data pipeline where raw data flows through separate analyst and data scientist scripts to produce inconsistent results.
A presentation slide illustrating the dbt macro workflow, displaying code examples for defining a macro, applying it in a query, and the resulting compiled SQL output.
A presentation slide displaying dbt testing code, featuring a custom SQL macro definition and a YAML configuration example for a column-level test.
A dbt documentation interface displaying the int_ecommerce__order_items_products model, its column definitions, and a lineage graph.
The curriculum guides learners through building a full dbt project from scratch, covering staging, intermediate, and mart layers.
Instruction includes sophisticated features such as incremental materialization, unit tests, and the use of external packages like dbt_utils.
Initial steps involving Python, Git, and BigQuery connections can be difficult due to technical complexity or UI changes in external tools. This signal predates the displayed course update; the update may have addressed it, but the update label does not prove that it was corrected.
Some learners find that complex YAML configurations and advanced concepts are introduced without sufficient individual conceptual breakdowns.
The course requires a level of technical comfort with Git and Python that may exceed the needs of absolute beginners.