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dbt

dbt — Zero to Advanced

A complete dbt track for beginners, analytics engineers, and data teams: models, sources, testing, documentation, Jinja, incremental models, snapshots, CI/CD, and production project structure.

Self-paced September 2026

◎Absolute beginners who need dbt explained without jargon

↗Analytics engineers building transformation pipelines

⚙Data engineers standardizing SQL across a team

◈Anyone prepping for an analytics engineering interview

20

Live modules

0

Queued modules

4

Sections

63+

Concepts covered

21h

Total reading

0

Prerequisites

This track starts with what dbt actually does, then builds toward production analytics engineering. You will learn what a transformation layer is, how models and the DAG work, how to test and document data, how to write reusable Jinja, and how real teams structure, test, and ship dbt projects in CI/CD.

Learning path

dbt Foundations

What dbt is, how it compiles and runs, project setup, models, sources, and ref().

Core Development

Materializations, incremental models, testing, documentation, and Jinja/macros.

Intermediate dbt

Packages, seeds, snapshots, environments, and hooks/operations.

Production and Advanced

Project structure, performance, CI/CD, testing strategy, and system design.

// Curriculum

20 Live Modules. 0 Modules Queued.

Follow in order. Each module begins with a simple explanation, then adds the production detail analytics engineers need.

1

Section 1 — dbt Foundations

MODULE 01LIVE

What is dbt?

dbt explained from scratch: what a transformation layer is, ELT vs ETL, and why analytics teams standardized on dbt.

ELTTransformationAnalytics engineering

60 min

Beginner

MODULE 02LIVE

How dbt Works: Compile, Run, and the DAG

What actually happens when you run dbt: Jinja compilation, the dependency graph, and the SQL dbt sends to your warehouse.

DAGCompilationdbt run

60 min

Beginner

MODULE 03LIVE

Setting Up a dbt Project

dbt_project.yml, profiles.yml, connecting to a warehouse, dbt Core vs dbt Cloud, and your first dbt run.

dbt_project.ymlprofiles.ymlSetup

60 min

Beginner

MODULE 04LIVE

Models: SELECT Statements as the Building Block

What a dbt model actually is, how a .sql file becomes a table or view, and naming/organization conventions.

ModelsSELECTConventions

60 min

Beginner

MODULE 05LIVE

Sources, ref(), and the Dependency Graph

Declaring raw sources, source freshness, and how ref()/source() build the DAG that makes dbt run models in the right order.

source()ref()DAG

60 min

Beginner

2

Section 2 — Core dbt Development

MODULE 06LIVE

Materializations: View, Table, Incremental, Ephemeral

What SQL each materialization compiles to, and how to choose the right one for a model.

MaterializationsViewTableEphemeral

65 min

Intermediate

MODULE 07LIVE

Incremental Models in Depth

is_incremental(), unique_key, incremental strategies (merge/delete+insert/append), and full-refresh recovery.

Incrementalis_incremental()unique_key

70 min

Intermediate

MODULE 08LIVE

Testing: Generic and Singular Tests

unique, not_null, accepted_values, relationships, custom generic tests, and singular SQL tests.

Testsuniquenot_nullrelationships

65 min

Intermediate

MODULE 09LIVE

Documentation: Descriptions, Doc Blocks, and dbt Docs

schema.yml descriptions, doc blocks, the dbt docs site, and documentation as a team habit, not an afterthought.

Documentationdbt docsschema.yml

55 min

Intermediate

MODULE 10LIVE

Jinja and Macros: Templating SQL

Jinja control flow inside SQL, writing reusable macros, and the line between "helpful templating" and "unreadable SQL".

JinjaMacrosTemplating

70 min

Intermediate

3

Section 3 — Intermediate dbt

MODULE 11LIVE

Packages and dbt_utils

Installing community packages, packages.yml, dbt_utils highlights, and when to write your own vs reuse one.

Packagesdbt_utilspackages.yml

55 min

Intermediate

MODULE 12LIVE

Seeds: Loading Static Reference Data

What seeds are for, CSV-to-table loading, seed configs, and where seeds stop being the right tool.

SeedsCSVReference data

50 min

Intermediate

MODULE 13LIVE

Snapshots: Type 2 Slowly Changing Dimensions

How dbt snapshots track history, timestamp vs check strategy, and the SCD Type 2 columns dbt manages for you.

SnapshotsSCD Type 2History

65 min

Intermediate

MODULE 14LIVE

Variables and Environments

vars, env_var(), target contexts, and running the same project safely across dev, staging, and prod.

VariablesEnvironmentstarget

55 min

Intermediate

MODULE 15LIVE

Hooks and Operations

pre-hook, post-hook, on-run-start/end, and run-operation for grants, audit logging, and one-off maintenance.

Hooksrun-operationGrants

55 min

Intermediate

4

Section 4 — Production and Advanced dbt

MODULE 16LIVE

Project Structure and Layering

Staging, intermediate, and marts layers; folder conventions; and how a project stays sane past 200 models.

StagingIntermediateMartsStructure

65 min

Advanced

MODULE 17LIVE

Performance and Query Optimization in dbt

Finding slow models, materialization trade-offs at scale, incremental strategy tuning, and warehouse-specific configs.

PerformanceQuery optimizationIncremental strategy

70 min

Advanced

MODULE 18LIVE

CI/CD for dbt Projects

Slim CI, dbt build in a pipeline, state comparison (--defer, --state), and safe deploys to production.

CI/CDSlim CI--defer

70 min

Advanced

MODULE 19LIVE

Testing Strategy and Data Quality at Scale

Where to test, test severity, freshness SLAs, and building a data-quality culture instead of a pile of assertions.

Data qualityTest strategySeverity

65 min

Advanced

MODULE 20LIVE

dbt Interview and System Design Guide

Worked system-design questions, a vocabulary cheat sheet, common interview traps, and rapid-fire prep — the capstone module.

Interview prepSystem designCapstone

90 min

Advanced

Recommended foundation before advanced dbt projects

dbt becomes much easier when you also understand SQL, data warehouses, and pipeline design. This track teaches dbt directly, then points naturally into the Snowflake and Data Engineering paths.

Explore Snowflake →

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