dbt & Analytics Engineering
The DAG, materializations, incremental models, and tests that make SQL transformations behave like software
A practitioner's tour of dbt and the analytics engineering discipline it created: what analytics engineering is and why it sits between data engineering and analysts, how ref() and source() build a dependency DAG out of plain SQL, the four materializations and when each one earns its cost, incremental model strategies (merge vs insert_overwrite, unique_key, is_incremental()) with a worked example, the testing and documentation layers that make a warehouse trustworthy, and the staging/intermediate/marts project structure nearly every serious dbt project converges on.
Practice questions (4)
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Debugging an Incremental Model That Produces Duplicate Rows
Intermediate · Free -
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Choosing the Wrong Materialization Is Burning Warehouse Spend
Intermediate -
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A Model Is Missing From the Lineage Graph and Running Out of Order
Intermediate -
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Reviewing a PR That Breaks the Staging/Intermediate/Marts Layering
Intermediate