dbt Analytics Engineering Certification Training
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M1 — The dbt Project and the Model Layers
Project structure: models, macros, tests, seeds, snapshots and analyses
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The staging layer: light cleaning, one source per model, no business rules
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The intermediate layer: reusable logic that keeps the marts thin
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The mart layer: business-shaped tables and the consumers who read them
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Materialisations: view, table, incremental and ephemeral and their costs
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Naming conventions and folder structure a reviewer can navigate
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M2 — Sources, Ref and the Dependency Graph
Declaring sources in a YAML properties file and describing each one
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source() and ref() and the compiled SQL that Jinja renders from them
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Reading the manifest and the lineage graph dbt builds from your project
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Source freshness checks and the timing that makes them meaningful
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Adding a new source and working out the blast radius of a changed column
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Disabling a model and what that does to everything downstream of it
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M3 — Tests: Built-in, Generic and Singular
Built-in tests: not_null, unique, accepted_values and relationships
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Generic tests with custom arguments, severity and a where clause
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Singular tests written as SQL files that return the failing rows
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Writing a test for a business rule such as a credit limit
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Coverage gaps that a dbt documentation page makes obvious
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Fixing a failure in order: correct the model first, then adjust the test
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M4 — Jinja, Macros and Reusable SQL
Jinja in dbt: expressions, filters, tests and the render context
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Writing a macro with arguments, defaults and a documentation block
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Building a reusable metric macro that several marts call
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The packages directory and installing a community package you trust
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Hooks, ephemeral models and the job each of them is right for
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Debugging compiled SQL with the run and compile commands
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M5 — Incremental Models and Merge Strategies
The incremental materialisation and the unique key that drives updates
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Merge strategies: insert, update, delete+insert and the rows each touches
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Filtering on the batch window and the rows that never get refreshed
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Full refresh, its cost and when a scheduled rebuild is the safer answer
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Building a staging incremental that survives late-arriving source rows
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Reviewing materialisation choices across a project as one cost decision
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M6 — Snapshots, Seeds and Change Tracking
Snapshot strategies: check, timestamp and the history each one keeps
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Reading the check columns and the dbt_valid_to end date
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Rebuilding a snapshot and the history it can quietly destroy
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Seeds: small reference CSV files loaded and tested inside the project
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Turning a snapshot table into a slowly changing dimension for the mart
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Applying a soft delete without losing the historical rows
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M7 — Exposures, Documentation, Semantic Layer and dbt Cloud
Exposures: declaring the dashboard or metric that depends on a model
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docs generate and the data dictionary each model owner publishes
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Semantic layer: semantic models, metrics and the saved query behind them
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dbt Cloud environments: development, testing and production
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Deployment jobs, the continuous integration flow and what a check catches
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Connection settings, environment variables and permissions in dbt Cloud
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M8 — Capstone: Ship a Tested dbt Project to dbt Cloud
Declare three sources with freshness and document their ownership
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Build staging, intermediate and mart models with the right materialisations
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Add generic and singular tests for the business rules that matter
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Convert the largest model to incremental and prove the row counts hold
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Add a snapshot and an exposure, then publish the docs and lineage graph
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Deploy through a dbt Cloud environment and run the full test suite
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