Name: dbt (Data Build Tool): The Analytics Engineering Guide
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Created byWadson Guimatsa

Last updated 1/2025

English

What you'll learn

Managing dbt Projects: Learn to initiate, structure, and effectively manage dbt projects, including dbt profiles understanding.
Master dbt Models: Understand how to create and manage dbt models, including their dependencies, configurations.
Grasp dbt's Core Purpose: You will confidently articulate what dbt is and its crucial role in data engineering.
Implement Testing in dbt: Understand the different types of tests in dbt, and how to implement them effectively for different models and other dbt resources..
Understand dbt Packages: Gain knowledge on how to use dbt packages to modularize and reuse code across different dbt projects.
Deploy dbt Cloud Jobs: Learn how to configure and deploy dbt jobs in various environments, understanding the differences and requirements of each.
Create and Maintain dbt Documentation: Learn how to generate and maintain documentation within dbt, including descriptions of sources, tables, and columns.
Setting Up and Installing dbt: you should be able to navigate the process of installing dbt and setting it up whether that's a local machine or dbt cloud
Version Control: Understand how dbt integrates with platforms like GitHub to provide version control, ensuring you can track and manage changes effectively.
Streamlined Workflows: Instead of juggling multiple tools and platforms, learn how dbt serves as a one-stop solution for most of your data transformation needs.
dbt Cloud IDE: Master how to use dbt Cloud IDE to write, test, and deploy DBT models and other resources without needing to interact with the command line.

Course content

7 sections • 66 lectures • 5h 48m total length

Introduction1:06
In this section, we will set up the necessary components to learn dbt.
We'll create a BigQuery data warehouse on Google Cloud, load data, and configure permissions. Then, we'll create a dbt Cloud account, connect it to BigQuery, and link our dbt project to a GitHub repository.
Resources and Guidelines for the Course0:29
Link to project repository: https://github.com/insightahead/dbt-learning-project
Create and Setup a Google Cloud Account8:01
We'll create a Google Cloud account, set up a BigQuery instance, and load data into the instance for dbt to later transform.
We'll also create a storage bucket to store data and ensure proper access settings.
Finally, we'll create a BigQuery dataset and tables within it to prepare for working with dbt.
Create Tables in Google BigQuery5:20
In this lecture, we created tables inside our dataset using data from the files uploaded to our bucket.
Instead of manually creating 24 tables, we used a script to automate the process.
Create a dbt Cloud Account5:28
In this lecture, we'll create a dbt cloud account and link it to our BigQuery instance.
We'll sign up for a free dbt cloud account, create a project, and choose BigQuery as our data warehouse. Next, we'll configure the connection by creating a service account JSON file with the necessary credentials in BigQuery. After uploading the JSON file to dbt and testing the connection, we'll have successfully set up our dbt cloud account
Create a GitHub Account4:57
In this lecture, we will set up our code repository using GitHub.
We will create a GitHub account, link it to our dbt Cloud account, and configure the repository settings.
We will then create a new repository on GitHub and connect it to our dbt Cloud project.
To ensure everything is correctly set up, we run a dbt command in the dbt Cloud IDE to build our first model in BigQuery.
About the Dataset0:41

What is a dbt Models1:57
After attending this lecture, you will gain an understanding of SQL models in dbt and their role in organizing business logic using SELECT statements and Common Table Expressions (CTEs).
Creating Your First DBT Model8:11
By the end of this lecture, you will be able to set up a development environment in dbt, create branches, and use a Git workflow to manage your dbt project.
You will also be able to create and configure a simple dbt model, write SQL queries to manipulate and transform data and preview and execute models on a data warehouse platform like Google BigQuery.
Staging Models Fundamentals in dbt2:12
After completing this lecture, you will understand the importance of staging in a data engineering pipeline and learn how to model the staging area in dbt.
You will explore the different transformations applied in the staging area.
Intermediate Models: Reading Assignment0:03
dbt Sources: Introduction2:47
By the end of this lecture, you will be able to identify the challenges and limitations of directly selecting raw data in dbt models.
You will learn about declaring and configuring a dbt source in a YAML file.
Additionally, you will explore how to use the source function to reference sources in dbt models for improved data management and gain hands-on experience in building your first source in dbt to enhance data pipeline flexibility and maintainability.
Creating and Configuring dbt Sources: A Step-by-Step Introduction4:03
By the end of this lecture, you will be able to create and configure a dbt source YAML file, understand the importance of accurate indentation in YAML files, and learn how to troubleshoot parsing errors.
dbt Sources: How to Use the Source Function5:29
By the end of the lecture, you will be able to:
Utilize the source function effectively in data models.
Troubleshoot and fix common errors in data models.
Differentiate between project name and project ID in Google BigQuery.
Run and test SQL queries in the BigQuery editor.
Create and maintain data lineage in data models.
Apply version control practices for data model changes.
dbt Source Testing Essentials: Ensuring Data Quality10:39
By the end of this lecture, you will be able to:

Add tests to a dbt source for data validation.
Use UNIQUE and NOT NULL tests to ensure data integrity.
Implement ACCEPTED VALUE tests for columns with a limited set of values.
Debug and resolve issues with test execution.
Utilize dbt Cloud IDE to run tests and analyze test results.
dbt Packages: Leverage existing code for Efficient Analytics Workflows4:55
Understand the concept of dbt packages and their benefits.
Utilize the dbt hub for finding and implementing packages in your project.
Utilizing dbt Packages: Generating Sources and Staging Models8:10
Understand how to add the dbt code generation package to a project and create a packages.yml file.
Learn how to call and use macros in dbt, such as the generate source macro.
Explore ways to customize the generated source definitions, such as adding column names and descriptions.
Learn how to utilize the dbt hub and its packages to create staging models for each source in a project efficiently.
dbt Code-Gen Package: Efficiently Generating Staging Models8:12
Documenting Your dbt Project: How to Document Models and Sources5:29
Understand the importance of documentation in dbt projects.
Learn how to add descriptions to sources, models, tables, and columns.
Discover how to generate and view the documentation for your dbt project.
Navigate the documentation page to understand the structure of your project and its components.
Documenting Your dbt Models: Best Practices and Tips14:17
ref function in dbt: Introduction1:56
Understand the purpose and importance of the ref function in dbt.
Understanding the ref function6:59
Learning how to reference existing models using the ref function and Jinja syntax in dbt
Learning how to build a Directed Acyclic Graph (DAG) in dbt to visualize the dependencies between models
dbt-codegen Package: Using the generate_model_yaml macro4:53
Learn how to use the dbt-codegen package to generate YAML for models.
Familiarize yourself with other useful macros available in the dbt-codegen package.
Know how to add descriptions, documentation, and tests to models in YAML files
Collaborating with Your Team Using Pull Requests in GitHub6:35
Understand the purpose of a pull request or a merge request
Understand the importance of code review and collaboration when merging changes to the main branch
Be aware of the concept of environments in dbt and understand their purpose in deployment
dbt environments: Introduction2:16
Understand the concept of environments in dbt.
dbt Cloud: Setting Up a Deployment Environment3:27
Understand the purpose of deployment environments in dbt.
Navigate to the environments section in dbt Cloud IDE.
Create a production environment with a specific dbt version.
Configure deployment credentials according to the warehouse being used.
dbt Jobs: Creating and Running dbt Jobs in Deployment Environments6:58
Understand the components and purpose of a dbt job in a deployment environment.
Learn how to create and configure a dbt job, including specifying the environment, target name, and execution settings.
Explore the dbt build command and its role in running models, tests, snapshots, and seeds.
Execute a dbt job and monitor its progress from the queued state to successful completion.
dbt Jobs: Scheduling for Automated Execution5:10
Learn how to configure a dbt job to run on a schedule, including selecting specific days and times.
Explore custom cron schedules for more precise control over job execution timings.
Monitor and manage scheduled jobs, including viewing run history and generated documentation.

dbt Core Prerequisites: Git, Python and Google Cloud CLI1:01
dbt Core: Installation4:36
dbt Core: Initializing the GCloud CLI3:19
dbt Core: Create Profiles Manually4:22
dbt Core: dbt init Command - Create Profiles and Project Automatically0:12
The dbt init command initializes a new dbt Core project
dbt Core - Initial Local Run2:13
dbt Core: Show Command - CLI Only4:08
dbt Core: Clean Command - CLI Only4:00
dbt clean deletes folders listed in the clean-targets of dbt_project.yml, often used for dbt_packages and target directories. It doesn't work with the dbt Cloud IDE'.
In dbt Cloud, dbt deps auto-cleans before installing packages, and the target folder can be removed manually from the sidebar.

Introduction to project Configuration6:01
Project Configuration Part I8:47
Resource Configurations and Properties4:55
Model Configuration: Config Block - Table Materialization4:19
Resource Configuration: Property File - Table Materialization5:39
Resource Configuration: DBT Project File - Adding Tags11:52
Resource Configuration: DBT Project File - Using the Meta Configuration10:29
Incremental Models - Introduction1:10
Incremental Models - Setup2:46
Incremental Models - Implementation Part I7:51
Incremental Models - Implementation Part II1:53
Incremental Models - Implementation Part III5:05
Incremental Models - Implementation Part IV5:09
Ephemeral Models10:05

Requirements

Foundational SQL Knowledge: While the course will delve into dbt, which builds upon SQL, students should be comfortable with basic SQL queries, joins, and aggregations
Hands-On Approach: An inclination to apply knowledge practically will be beneficial.
Willingness to Learn and Install Software: While the course will guide through the essentials, students should be open to installing and exploring new software and tools as required.

Description

Take your skills as a data professional to the next level with this Hands-on Course course on dbt, the Data Build Tool.

Start your journey toward mastering Analytics Engineering by signing up for this course now!

This course aims to give you the necessary knowledge and abilities to effectively use dbt in your data projects and help you achieve your goals.

This course will guide you through the following:

Understanding the dbt architecture: Learn the fundamental principles and concepts underlying dbt.
Developing dbt models: Discover how to convert business logic into performant SQL queries and create a logical flow of models.
Debugging data modeling errors: Acquire skills to troubleshoot and resolve errors that may arise during data modeling.
Monitoring data pipelines: Learn to monitor and manage dbt workflows efficiently.
Implementing dbt tests: Gain proficiency in implementing various tests in dbt to ensure data accuracy and reliability.
Deploying dbt jobs: Understand how to set up and manage dbt jobs in different environments.
Creating and maintaining dbt documentation: Learn to create detailed and helpful documentation for your dbt projects.
Promoting code through version control: Understand how to use Git for version control in dbt projects.
Establishing environments in data warehouses for dbt: Learn to set up and manage different environments in your data warehouse for dbt projects.
Testing Data Models: Learn how to use built-in tests in dbt and create custom ones.

By the end of this course, you will have a solid understanding of dbt, be proficient in its use, and be well-prepared to take the dbt Analytics Engineering Certification Exam. Whether you're a data engineer, a data analyst, or anyone interested in managing data workflows, this course will provide valuable insights and practical knowledge to advance your career.

Please note that this course does not require any prior experience with dbt. However, familiarity with SQL and basic data engineering concepts will be helpful.

Disclaimer:
This course is not affiliated, associated, authorized, endorsed by, or in any way officially connected with dbt Labs, Inc. or any of its subsidiaries or its affiliates. The name “dbt” and related names, marks, emblems, and images are registered trademarks of dbt Labs, Inc. Similarly; this course is not officially connected with any data platform or tools mentioned in the course. The course content is based on the instructor's experience and knowledge and is provided only for educational purposes.

Who this course is for:

Beginners in data analytics who are starting their journey with data processing tools and are looking for a thorough understanding of dbt.
SQL practitioners of all levels looking to comprehensively incorporate dbt into their data processing toolset.
Business analysts who work with data regularly and aim to optimize their workflow with a more in-depth understanding of dbt.
Data engineers and data scientists enthusiastic about harnessing dbt's complete capabilities for improved ETL/ELT workflows, testing, and analytics.
Professionals transitioning into data roles and seeking a hands-on introduction to a popular data build tool.

What you'll learn

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Course content

Introduction and dbt setup7 lectures • 26min

Developing dbt models21 lectures • 1hr 55min

dbt Core8 lectures • 24min

Configuring dbt Project14 lectures • 1hr 26min

Analyses & Seeds3 lectures • 23min

Node Selection Syntax6 lectures • 33min

dbt Testing: How to test your dbt resources7 lectures • 42min

Requirements

Description

Who this course is for: