Mastering dbt - A Practical Study Guide for Learners and Exam Prep

Finding the encouragement to study for a certification on your own can be tricky. Seeing the lack of a comprehensive study guide for the dbt certification, I decided to create my own.

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I am an experienced Data Analyst looking to upskill in analytics engineering, which is why I decided to obtain the dbt Analytics Engineering Certification.

However, while dbt has an official study guide and there are some resources online, I still felt that studying for this certification could be a bit more straightforward. Besides, no one really details the practice part. The official study guide recommends at least 6 months of practical experience with dbt, so reviewing documentation alone might not get you very far.

Therefore, I decided to come up with my own study guide and share it online with fellow Analysts who, like me, feel a bit overwhelmed by the topics covered in the exam. I am also going to be sharing my study notes, including steps to practice on a real project, so you have material to use, improve, and motivate you to keep a consistent study routine.

Bear in mind, I have not taken the exam yet, so I can’t promise this is a tried-and-true method to pass it. But my study guide was built after countless hours of research, and I will be adjusting it as I go, if necessary.

Below, you will find the links to my study guide, my notes, and other useful resources. Links to the notes will be added weekly on Mondays to this post, or you can subscribe to my newsletter on Substack to receive them straight to your email.

Table of contents

• My dbt Analytics Engineering Certification Study Guide

• Study notes (in progress):

  Checkpoint 1:

  • 1.1 Data modeling techniques for more modularity

  • 1.2 DRY principles and how to write efficient SQL

  • 1.3 Your essential dbt project checklist

  • 1.4 How we structure our dbt projects

  • 1.5 Getting started with git branching strategies and dbt

  Checkpoint 2:

  • 2.1 Untangling a messy DAG

  • 2.2 Best practice for workflows

  • 2.3 Configs and properties, Resource path, and Plus prefix

  • 2.4 The dbt_project.yml file

  • 2.5 Materialisations - Views, Tables and Ephemeral

    Models:

  • 2.6 What is a dbt model and how they can be configured

  • 2.6.1 Defining model properties

  • 2.6.2 Understanding the run command

    Sources:

  • 2.7 An overview of Sources

  • 2.7.1 An overview of Source properties

  • 2.7.2 Configuring your Sources

  • 2.7.3 Setting up and using source freshness

    Commands:

  • 2.8 The 4 key dbt commands and node selectors

    Debugging:

  • 2.9 Interpreting logs and fixing common errors

  Checkpoint 3:

  • 3.1 Refactoring SQL: making legacy code modular

  Checkpoint 4:

  • 4.1 Understanding project dependencies and Packages

  • 4.2 Materialisations: incremental

  • 4.3 A deep dive into Seeds

  • 4.4 Setting up and maintaining Snapshots

  • 4.5 All you need to know about Exposures

  • 4.6 Model Governance: access, contracts and namespaces

  • 4.7 How and when to use Model Versioning

  • 4.8 Managing access permissions with Grants

  • 4.9 Generating documentation with dbt docs

  • 4.10 Python models

  • 4.11 Jinja & Macros within dbt

  Checkpoint 5:

  • 5.1 Adding data tests to your DAG

  • 5.2 Data Tests Properties

• Practice Project tasks (in progress):

  • Checkpoint 1

  • Checkpoint 2

  • Checkpoint 3

  • Checkpoint 4 - part 1

  • Checkpoint 4 - part 2

  • Checkpoint 4 - part 3

  • Checkpoint 4 - part 4

  • Checkpoint 4 - part 5

  • Checkpoint 5 - in progress

This is the main part of what I wanted to share, so happy studying! But if you want to know a bit more about me, my background and the research I did to build my study guide, read on.

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About me and my dbt experience:

Why did you decide to get the dbt Analytics Engineering Certification?

Recently, I quit my job and became a freelancer because I was feeling stagnated. I felt like the market was moving on while my job was only requiring skills that I had mostly mastered.

With 6 years of Business Intelligence experience under my belt, I started by diving into flow orchestration, improving my SQL skills, and dipping my toes into Data Engineering.

It was during the Data Engineering Zoomcamp – a great hands-on, community-driven initiative, by the way – that I first learned about Analytics Engineering and dbt. While we learned to build a full data workflow, I noticed that I had a lot of fun creating data models on dbt. This is how I decided to get the dbt Analytics Engineering Certification.

How much dbt experience do you have?

I have no working experience with dbt. I would say I have intermediate SQL skills with some on-the-job experience, and I do have a solid data analysis background working in different industries for companies in the UK, France, and Italy. I also took the Data Engineering Zoomcamp, which was my first exposure to dbt and data modelling and led me to create some models for my final project.

Have you taken any dbt courses online?

I have taken the dbt Fundamentals course and have completed the dbt Certified Developer learning path. I would say these are essential steps, even if you are an experienced dbt developer, because it takes you through the flow of the exam and gives you a pretty good idea of what you will find there.

However, all the resources I found indicate that these two courses are not enough to get you ready for the certification, especially if you are inexperienced.

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Resources that informed my study guide:

Obviously, the dbt certificate study guide is the starting point for anyone wanting to take the exam. However, I improved it by adding insights from people who actually took and passed the exam.

If you’ve been researching preparation tips for the exam, you have definitely come across this article by Rajeev Rajaram. The author gives an overview of how he prepped and passed the exam and suggests additional documentation that wasn’t in the official guide. These, as well as some of the blog posts and documentation suggested by Michiel Smulders and Paul Fry and were incorporated into my guide.

If you’re wondering how the exam is laid out, this article shares more information on the types of questions you may find. Dakota Kelley also shared information on the exam as well as suggestions for practice exercises. She actually failed the certification in her first try and mentioned that the practical part of building models with online datasets is what made her pass the second time.

You may also be interested in taking a practice exam online. The only somewhat reliable practice exam for this certification I could find is this one from qanalabs. I will be trying it once I advance more into my studies, but Daan Vandenreyt shares his experience with mixed feelings towards it.

This is it for now. Keep coming back to this post for new weekly material to help you study for your dbt certification, or subscribe to my newsletter to get them as they are published straight into your inbox!