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Home - Lessons - dbt Analytics Engineering Certification Training - M2 — Sources, Ref and the Dependency Graph - Source freshness checks and the timing that makes them meaningful

dbt Analytics Engineering Certification Training Source freshness checks and the timing that makes them meaningful

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

The staging layer: light cleaning, one source per model, no business rules

The intermediate layer: reusable logic that keeps the marts thin

The mart layer: business-shaped tables and the consumers who read them

Materialisations: view, table, incremental and ephemeral and their costs

Naming conventions and folder structure a reviewer can navigate

M2 — Sources, Ref and the Dependency Graph

Declaring sources in a YAML properties file and describing each one

source() and ref() and the compiled SQL that Jinja renders from them

Reading the manifest and the lineage graph dbt builds from your project

Source freshness checks and the timing that makes them meaningful

Adding a new source and working out the blast radius of a changed column

Disabling a model and what that does to everything downstream of it

M3 — Tests: Built-in, Generic and Singular

Built-in tests: not_null, unique, accepted_values and relationships

Generic tests with custom arguments, severity and a where clause

Singular tests written as SQL files that return the failing rows

Writing a test for a business rule such as a credit limit

Coverage gaps that a dbt documentation page makes obvious

Fixing a failure in order: correct the model first, then adjust the test

M4 — Jinja, Macros and Reusable SQL

Jinja in dbt: expressions, filters, tests and the render context

Writing a macro with arguments, defaults and a documentation block

Building a reusable metric macro that several marts call

The packages directory and installing a community package you trust

Hooks, ephemeral models and the job each of them is right for

Debugging compiled SQL with the run and compile commands

M5 — Incremental Models and Merge Strategies

The incremental materialisation and the unique key that drives updates

Merge strategies: insert, update, delete+insert and the rows each touches

Filtering on the batch window and the rows that never get refreshed

Full refresh, its cost and when a scheduled rebuild is the safer answer

Building a staging incremental that survives late-arriving source rows

Reviewing materialisation choices across a project as one cost decision

M6 — Snapshots, Seeds and Change Tracking

Snapshot strategies: check, timestamp and the history each one keeps

Reading the check columns and the dbt_valid_to end date

Rebuilding a snapshot and the history it can quietly destroy

Seeds: small reference CSV files loaded and tested inside the project

Turning a snapshot table into a slowly changing dimension for the mart

Applying a soft delete without losing the historical rows

M7 — Exposures, Documentation, Semantic Layer and dbt Cloud

Exposures: declaring the dashboard or metric that depends on a model

docs generate and the data dictionary each model owner publishes

Semantic layer: semantic models, metrics and the saved query behind them

dbt Cloud environments: development, testing and production

Deployment jobs, the continuous integration flow and what a check catches

Connection settings, environment variables and permissions in dbt Cloud

M8 — Capstone: Ship a Tested dbt Project to dbt Cloud

Declare three sources with freshness and document their ownership

Build staging, intermediate and mart models with the right materialisations

Add generic and singular tests for the business rules that matter

Convert the largest model to incremental and prove the row counts hold

Add a snapshot and an exposure, then publish the docs and lineage graph

Deploy through a dbt Cloud environment and run the full test suite

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