Mastering dbt - Full Study Guide

Part of the “Mastering dbt - A Practical Study Guide for Learners and Exam Prep” series. Let’s connect on LinkedIn!

This study guide outlines documentation to be reviewed in preparation for the dbt Analytics Engineering Certification Exam, as well as steps to be implemented on a dummy project as you go. It was built after extensive research and may be updated, if necessary.

The resources used to build this guide, as well as any updates, can be found in the initial post of the “Study for the dbt Analytics Engineering Certification with me” series.

Finally, study notes will be linked to this guide as I write them. Again, refer to the initial post for any updates.

Before you start following this guide:

Before following the schedule below, start with these two courses:

• Dbt Fundamentals

• Certified Developer Learning Path

Checkpoints:

1. Pre-requisites:

Conceptualizing modularity and how to incorporate DRY principles

• Data modelling techniques for more modularity – (study notes)

• DRY principles: how to write efficient SQL - (study notes)

Understanding the structure of a data modelling project:

• Your Essential dbt Project Checklist - (study notes)

• How we structure our dbt projects - (study notes)

Git functionalities:

• Getting Started with git Branching Strategies and dbt - (study notes)

Practice:

• Practice Project: Checkpoint 1

2. The Basics

Creating a logical flow of models and building clean DAGs

• DAG use cases and best practices and How to audit your DAG (video) - (study notes)

Understanding dependencies and materialisations

• Best practice for workflows - (study notes)

• Materialisations - Views, Tables and Ephemeral - (study notes)

Overview of configs and properties:

• Configs and properties, Resource path, and Plus prefix - (study notes)

• Dbt_project.yml - (study notes)

Resource deep-dive: models

• About DBT models and Models configurations - (study notes)

• Model properties - (study notes)

• run command - (study notes)

Resource deep-dive: sources

• Adding Sources to your DAG - (study notes)

• Source properties - (study notes)

• Sources configurations - (study notes)

• Source freshness command - (study notes)

Using commands

• Node selection - (study notes)

Understanding logged error messages and troubleshooting

• Events and logs and Debugging errors - (study notes)

Practice:

• Practice Project: Checkpoint 2

3. Modularity and Refactoring

Converting business logic into performant SQL queries

• Refactoring legacy SQL to dbt - (study notes)

Practice:

• Practice Project: Checkpoint 3 - to be added

4. Doing more with dbt

Using dbt Packages

• Packages

Advanced materialisations

• Materialisations - incremental and materialised views

Resource deep-dive: seeds

• Add Seeds to your DAG

• Seeds configurations

• Seeds properties

• seed command

Resource deep-dive: snapshots

• Add Snapshots to your DAG

• Snapshot configurations

• Snapshot properties

• snapshot command

Resource deep-dive: exposures

• Add Exposures to your DAG

• Exposure properties

Understanding dbt models governance

• Model Governance

• Model contracts

• Model versions

• Model access

Providing access to users to models with the “grants” configuration

• Grants

Creating and maintaining dbt documentation

• About documentation and About doc function

• dbt docs command

• Using macros to show model and data lineage on the DAG (No documentation available, but it is a topic in the official study guide)

Creating Python models

• Python models

Practice:

• Practice Project: Checkpoint 4 - to be added

5. Deployment and testing

Using generic, singular, custom, and custom generic tests on a wide variety of models and sources

• Why is data testing necessary?

• Add data tests to your DAG

• Data test properties

• Data test configurations

• test command and Test selection examples

Leveraging the dbt state

• State method

• Tags

• Hooks & Operations

• About dbt retry command

Customising deployment

• Custom Schema

• Threads

• Target

• Schema

• Database

• Env_var

Utilizing git functionality within the development lifecycle

• The exact GitHub pull request template we use at dbt Labs

• How to review an analytics pull request

Using dbt clone

• About dbt clone command

Practice:

• Practice Project: Checkpoint 5 - to be added