The Complete dbt Data Build Tool Bootcamp Zero to Hero

Become a dbt™ professional with this ALL-IN-ONE COURSE covering both theory & practice through a real-world project!
The Complete dbt Data Build Tool Bootcamp Zero to Hero
File Size :
2.55 GB
Total length :
6h 9m



Zoltan C. Toth


Last update




The Complete dbt Data Build Tool Bootcamp Zero to Hero

What you’ll learn

Learn to use the dbt™ platform professionally through the creation of an exhaustive, real-world, hands-on dbt – Airbnb project covering both Theory and Practice
Set up the complete development environment on Mac & Windows, Connect to Snowflake and BI, Configure dbt profile, extend the IDE with dbt tools
Learn core dbt concepts such as Models, Materialization, Sources, Seeds, Snapshots, Packages, Hooks, Exposures, Analyses, write complex SQL queries
Understand the dbt project structure and learn about dbt tips & tricks, advanced techniques and best practices, extend dbt with your own / third-party macros
Implement singular and generic dbt tests, work with additional arguments and default config values, customize dbt built-in tests
Document your models and pipeline, customize the dbt docs page, Explore and analyse dependencies between transformation steps
Understand how dbt fits into the modern data stack, learn about the stages of the Data-Maturity Model, and well functioning Data Architectures
Master ETL/ELT procedures, Data Transformations, Modern Data Stack, Slowly Changing Dimensions, Common Table Expressions and Analytics Engineering
Understand what is a Data Warehouse, Data Lake, or Data Lakehouse and when to use which, handle Data Collection, Data Wrangling and Data Integrations
See how advanced testing works using dbt-expectations, a Great Expectations inspired testing framework

The Complete dbt Data Build Tool Bootcamp Zero to Hero


Basic SQL experience
No previous programming language experience required
Working computer (Mac/Windows/Linux)
Network access whitelist to snowflake(.com) and GitHub if you work behind a firewall or VPN
Git and Python (We are linking to the installation instructions of these tools in the course)


Become a dbt professional from scratch with this single course, solving a real-world problem step by step! We cover both theory and hands-on practice! Delivered by an instructor with 20+ years of Data Engineering experience   ✨✨ June 2023 update to the most recent version of dbt, 1.5! ✨✨”Excellent course! Edit: I managed to pass the dbt certification exam. I couldn’t have done it without your help! Again, it’s an awesome course!”⭐️⭐️⭐️⭐️⭐️ Agnit Chatterjee”Fantastic course. Well-chosen examples perfectly illustrate the many features that are covered. The pacing is spot on and it is easy to replicate the examples.”⭐️⭐️⭐️⭐️⭐️ Ricky McMaster”I love how you’re explaining everything at just the right level!”⭐️⭐️⭐️⭐️⭐️ William JahnGreetings to the MOST COMPLETE, CONTINUOUSLY UPDATED independent dbt™ (Data Build Tool) software course in the world – as of 2023! This course is the TOP RATED and the BESTSELLER dbt course on Udemy! Thank you for joining us for The Complete dbt (Data Build Tool) Bootcamp: Zero to Hero – we are super excited to have you in the course!The structure of the course is designed to have a top-down approach. It starts with the Analytics Engineering Theory – all you need to know is to put dbt (Data Build Tool) in context and to have an understanding of how it fits into the modern data stack. We start with the big picture; then, we go deeper and deeper. Once you learn about the pieces, we are going to shift to the technicalities – a practical section -, which will focus on putting together the dbt “puzzle”. The practical section will cover each and every single dbt feature present today through the construction of a complete, real-world project; Airbnb. This presents an opportunity for us to show you which features should be used at what stage in a given project, and you will see how dbt is used in the industry.RECENT UPDATES:Course Updated to dbt 1.5 – May 2023Fully pastable course materials on GitHub and lecture notes added about common pitfalls in dbt setup – Jan 2023Added Great Expectations and test debugging sections – Sep 2022Radically simplified Windows installation instructions (no WSL needed anymore)  – Sep 2022The course is tested in dbt cloud – Aug 2022Added Modern Data Stack overview – Jun 2022 THEORETICAL SECTION:Among several other topics, the theoretical section puts special emphasis on transferring knowledge in the following areas;Data-Maturity ModelWell-functioning Data ArchitecturesData Warehouses, Data Lakes, and Data LakehousesETL and ELT procedures and Data TransformationsFundamentals of dbt (Data Build Tool)Analytics EngineeringModern Data StackSlowly Changing DimensionsCTEsOnce we understood the theoretical layer and how dbt fits into the picture, we are going to start building out a dbt project from scratch, just as you would do this in the real world.PRACTICAL SECTION:The practical section will go through a real-world Airbnb project where you will master the ins and outs of dbt! We put special focus on getting everyone up and ready before the technical deep dive, hence we will start off by setting up our Development Environment:MAC Development Environment SetupWINDOWS Development Environment SetupIDE dbt Extension InstallationCreation and Activation of Virtual EnvironmentsSetting up SnowflakeOnce we are ready – among several other technical topics, the following features will be covered;dbt Modelsdbt Materializationsdbt Testsdbt Documentationdbt Sources, Seeds, Snapshotsdbt Hooks and OperationsJinja and Macrosdbt PackagesAnalyses, Exposuresdbt SeedsData Visualization (Preset)Working with Great Expectations (dbt-expectations)Debugging tests in dbtOnce the theory and the practical stages are finished, we are going to dive into the best practices and more advanced topics. The course is continuously updated, whenever dbt publishes an update we adjust the course accordingly, so you always be up to date!Who is this course for?Data EngineersData AnalystsData ScientistsBI DevelopersBI Analyst… and anyone who interacts with data lake/data warehouse/data lakehouse or uses SQL!Course Level Explained (Zero > Hero)The course doesn’t have any expectations about your abilities and starts education from zero. Every exercise is an unavoidable step in your studies. In the same way, don’t start an exercise of a superior level without having completed the preceding ones: you will be in difficulty if you do so. Practice is the only way to learn and it cannot be taken lightly. We are going to be next to you along the journey and you have our absolute support!When the Airbnb project is presented to you, you have to do it in its entirety, without omitting any guidelines and by understanding the objective. A project “almost completely” done is often a project “totally incomplete” for us. Give special attention to detail. Your only reliable source of information regarding the instructions is the pedagogical team, don’t trust the “I’ve heard”.By the time you complete the course, you will be equipped with both a very solid theoretical understanding and practical expertise with dbt. All the fundamentals, dbt features, best practices, advanced techniques and more will be covered in our course, which will make you become a master in dbt. Are you ready? ;)How to get help?We just published our initial round of Discussions on Udemy which is the easiest and most efficient way for you to post questions, receive answers, and peruse questions from other students. If you have questions or feedback, please reach out to us!That about wraps it up for us for now!Once again, thank you for being a part of this course. We can’t wait to get started with you soon!All the best,Zoltan C. Tothdbt Mark and the dbt logo are trademarks of dbt Labs, Inc.


Section 1: Course Introduction

Lecture 1 Instructors Introduction

Lecture 2 Welcome

Lecture 3 Course Structure Overview

Section 2: Theory – The Data Maturity Model

Lecture 4 Introduction – Maslow’s Pyramid of Data

Lecture 5 The Data Maturity Model

Lecture 6 ETL and ELT

Section 3: Theory – Data Warehouses, Data Lakes and Lakehouses

Lecture 7 Data Warehousing – a short introduction

Lecture 8 External Tables and Cloud Data Warehouses

Lecture 9 Data Lakes

Lecture 10 Data Lakehouse

Section 4: Theory – The Modern Data Stack

Lecture 11 The Modern Data Stack

Section 5: Theory – Slowly Changing Dimension (SCD)

Lecture 12 The Basics of Slowly Changing Dimensions

Lecture 13 Type 0 – Retain Original

Lecture 14 Type 1 – Overwrite

Lecture 15 Type 2 – Add New Row

Lecture 16 Type 3 – Add New Attribute

Section 6: Intro to the practical sessions: dbt and the Airbnb use-case

Lecture 17 dbt Overview

Lecture 18 Use-case and Input Data Model Overview

Section 7: Practice – Setup

Lecture 19 How to use github and the course’s resources

Lecture 20 Snowflake Registration

Lecture 21 A note on the Snowflake data import

Lecture 22 Importing Airbnb Data into Snowflake

Lecture 23 READ ME! Setup instructions and Prerequisites

Lecture 24 Optional – Installing Python and pip on Windows

Lecture 25 Optional – Setting up a Python Virtualenv on Windows

Lecture 26 Optional – Setting up Python and pip on a Mac

Lecture 27 dbt Installation (All Platforms)

Lecture 28 Creating a dbt 1.5 project and connecting it to Snowflake using dbt init

Lecture 29 READ ME – dbt project structure – data folder vs. seeds folder

Lecture 30 Overview of the dbt Project Structure

Lecture 31 Install dbt power tools into VSCode Setup (optional)

Lecture 32 A note on the DEV schema

Lecture 33 Datasets and Data Flow Overview

Section 8: Models

Lecture 34 Learning Objectives – Models

Lecture 35 Models Overview

Lecture 36 Theory: CTE – Common Table Expressions

Lecture 37 Creating our first model: Airbnb listings

Section 9: Materializations

Lecture 38 Learning Objectives – Materializations

Lecture 39 Materializations Overview

Lecture 40 Model Dependencies and dbt’s ref tag

Lecture 41 Table type materialization & Project-level Materialization config

Lecture 42 Incremental materialization

Lecture 43 Ephemeral materialization

Section 10: Seeds and Sources

Lecture 44 Learning Objectives – Seeds and Sources

Lecture 45 Seeds and Sources Overview

Lecture 46 Seeds

Lecture 47 Sources

Lecture 48 Source Freshness

Section 11: Snapshots

Lecture 49 Learning Objectives – Snapshots

Lecture 50 Snapshots Overview

Lecture 51 Creating a Snapshot

Section 12: Tests

Lecture 52 Learning objectives – Tests

Lecture 53 Tests Overview

Lecture 54 Generic Tests

Lecture 55 Singular Tests

Section 13: Macros, Custom Tests and Packages

Lecture 56 Learning Objectives – Macros, Custom Tests and Packages

Lecture 57 Macros Overview

Lecture 58 Creating our First Macro

Lecture 59 Writing Custom Generic Tests

Lecture 60 README updated versions of packages

Lecture 61 Installing Third-Party Packages

Section 14: Documentation

Lecture 62 Learning Objectives – Documentation

Lecture 63 Documentation Overview

Lecture 64 Writing and Exploring Basic Documentation

Lecture 65 Markdown-based Docs, Custom Overview Page and Assets

Lecture 66 The Linage Graph (Data Flow DAG)

Section 15: Analyses, Hooks and Exposures

Lecture 67 Learning Objectives – Analyses, Hook and Exposures

Lecture 68 Analyses

Lecture 69 Hooks

Lecture 70 Setting up a BI Dashboard in Snowflake and Preset

Lecture 71 Exposures

Section 16: dbt Hero

Lecture 72 Welcome to Hero

Lecture 73 Have your say in the course’s roadmap

Section 17: Debugging Tests and Testing with dbt-expectations

Lecture 74 A note on the dbt-expectations setup

Lecture 75 Great Expectations Overview

Lecture 76 Comparing row counts between models

Lecture 77 Looking for outliers in your data

Lecture 78 Implementing test warnings for extremal items

Lecture 79 Validating column types

Lecture 80 Monitoring categorical variables in the source data

Lecture 81 Debugging dbt tests and Working with regular expressions

Section 18: Best Practices for Introducing and Using dbt in your Company

Lecture 82 An interview with the Data Utilization Head of the Vienna Insurance Group

Section 19: dbt Certification Exam Preparation Guide

Lecture 83 How to prepare for the certification exam? An interview with Muizz Lateef

Section 20: Supplementary Materials

Lecture 84 Supplementary Material – Installing dbt on Windows with Windows Linux Filesystem

Analytics Engineers,Data Analysts,BI Analysts,Data Scientists,Data Engineers

Course Information:

Udemy | English | 6h 9m | 2.55 GB
Created by: Zoltan C. Toth

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