Data Build Tool Course

Lesson 1 • DBT Core vs. DBT Cloud

Distinguishes the open-source CLI tool from the managed cloud platform. Learners choose the right deployment mode for their environment.

Lesson 2 • Setting Up Your DBT Environment

Guides installation of DBT Core and connection to a data warehouse. Learners finish with a working local environment ready for project creation.

Lesson 3 • What Is DBT and Why It Matters

Covers DBT's core value proposition and how it transforms SQL-based data transformation. Grounds learners in the problem DBT solves before any hands-on work.

Lesson 1 • Initializing a DBT Project

Walks through dbt init and the generated file structure. Learners understand every auto-generated file's purpose before modifying anything.

Lesson 2 • Source Control and Project Conventions

Establishes Git-based workflows and naming conventions for scalable DBT projects. Learners apply consistent standards that support team collaboration.

Lesson 3 • Managing Profiles and Targets

Covers multi-environment profile setup for dev, staging, and production targets. Learners safely switch contexts without hardcoding credentials.

Lesson 1 • Organising Models into Layers

Introduces staging, intermediate, and mart layer conventions for model organisation. Learners structure a multi-layer project that mirrors real analytics engineering practice.

Lesson 2 • Materializations in Depth

Explains table, view, incremental, and ephemeral materializations with trade-offs. Learners select the right materialization for each model's performance needs.

Lesson 3 • Selecting and Executing Model Subsets

Covers node selection syntax for running targeted subsets of the DAG. Learners efficiently rebuild only the models they need during development.

Lesson 4 • Writing Your First DBT Model

Introduces the SQL SELECT-based model syntax and the ref() function. Learners write and execute a working model against a real warehouse table.

Lesson 1 • Source Freshness and Data Contracts

Teaches freshness thresholds and how to enforce data arrival SLAs at the source layer. Learners configure alerts that surface stale data before it reaches marts.

Lesson 2 • Declaring and Using Sources

Covers source YAML declarations and the source() function for referencing raw tables. Learners replace hardcoded table names with governed source references.

Lesson 3 • Capturing History with Snapshots

Introduces DBT snapshots for tracking slowly changing dimension records over time. Learners implement timestamp and check strategies for SCD Type 2 patterns.

Lesson 1 • Third-Party Testing Packages

Surveys popular packages like dbt-expectations and dbt-utils for extended test coverage. Learners install and apply package-based tests to close gaps in built-in coverage.

Lesson 2 • Writing Custom Generic Tests

Teaches how to author reusable Jinja-based generic tests for project-specific rules. Learners extend the test library beyond built-in options.

Lesson 3 • Singular Tests for Complex Logic

Introduces singular SQL-based tests for assertions that cannot be expressed generically. Learners write targeted tests for business-rule validation.

Lesson 4 • Test Severity and Failure Handling

Explains warn vs. error severity levels and how to handle expected test failures. Learners configure tests that alert without blocking pipeline execution.

Lesson 1 • Understanding and Using the DAG

Explains the directed acyclic graph view and how to use it for impact analysis. Learners trace data lineage from raw source to final mart model.

Lesson 2 • Using doc() Blocks for Reuse

Introduces Markdown doc blocks for writing long-form, reusable descriptions. Learners eliminate duplicate documentation across shared column definitions.

Lesson 3 • Writing Model and Column Descriptions

Covers inline YAML descriptions for models, columns, and sources. Learners document every asset so that generated docs are immediately useful.

Lesson 1 • Jinja Templating Fundamentals

Introduces Jinja syntax including variables, loops, and conditionals within SQL models. Learners write dynamic SQL that adapts to runtime context.

Lesson 2 • DBT Variables and Runtime Flags

Explains project-level and CLI-passed variables for parameterising model behaviour. Learners build models that change behaviour based on runtime inputs.

Lesson 3 • Writing and Using Macros

Covers macro definition, arguments, and invocation for reusable SQL logic. Learners replace repeated SQL patterns with single-source macro calls.

Lesson 4 • Hooks and Operations

Introduces pre-hook, post-hook, and on-run-end hooks for executing SQL around model runs. Learners automate warehouse maintenance tasks within the DBT lifecycle.

Lesson 1 • Continuous Integration for DBT Projects

Covers slim CI patterns using state comparison to test only modified models on pull requests. Learners configure a CI pipeline that runs fast, targeted checks.

Lesson 2 • Orchestrating DBT with Workflow Tools

Surveys integration patterns with orchestration platforms such as Airflow and Prefect. Learners trigger DBT runs from an orchestrator with proper dependency management.

Lesson 3 • Monitoring DBT Run Health

Covers run artifacts, metadata API, and alerting for failed runs and stale models. Learners build an observability layer that surfaces pipeline issues proactively.

Lesson 4 • Environment Promotion Strategies

Explains dev-to-staging-to-production promotion workflows and schema isolation. Learners deploy changes safely without overwriting production data during development.