The Complete dbt Bootcamp: Zero to Hero + Certification Prep
ALL-IN-ONE Bootcamp for Master dbt hands-on and get dbt Certification-ready
Lo que serás capaz de hacer
Become a dbt professional from scratch with this single course solving a real-world problem step by step, covering both theory and hands-on practice. Whether you want to level up your analytics engineering skills or earn the dbt Analytics Engineering Certification , this course takes you all the way: Zero to Hero. In a hurry? The basic and intermediate topics are compressed to the first 3-4 hours of the course. Want to get Certified? The course now covers every topic required in the 2026 Analytics Engineering Certification Exam and features a sample certification test. This is the MOST COMPLETE, CONTINUOUSLY UPDATED independent dbt course in the world, now fully aligned with the 2026 dbt Certification exam . Every certification topic is covered in depth. Not planning to get certified? No problem - the same hands-on lessons make you job-ready either way. Delivered by an instructor with 20+ years of Data Engineering experience. This is the TOP RATED and BESTSELLER dbt course on Udemy. "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." "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!" "I love how you're explaining everything at just the right level!" 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 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 will 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. 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 Setup WINDOWS Development Environment Setup Creation and Activation of Virtual Environments Installing dbt Setting up our Database Once we are ready - among several other technical topics, the following features will be covered; dbt Models dbt Materializations dbt Tests: Generic and Singular tests, Unit tests, Custom Tests, Data Contracts, and advanced testing techniques dbt Documentation dbt Sources, Seeds, Snapshots dbt Hooks and Operations Jinja and Macros dbt Packages dbt Fusion Visual Studio Code Extensions Analyses, Exposures dbt Seeds Data Visualization and BI (Preset) Working with Great Expectations (dbt-expectations) Debugging tests in dbt dbt Orchestration Python Models Constraints Comprehensive overview of Selectors Most useful commands for speeding up dbt work Microbatching Model Versioning, Deprecation and Disabling Models dbt in Production / Slim CI / defer / PRs / Production Best Practices Certification Preparation Guide and Practice Test sample Once the theory and the practical stages are finished, we will 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! THEORETICAL SECTION: Among several other topics, the theoretical section puts special emphasis on transferring knowledge in the following areas; Data-Maturity Model Well-functioning Data Architectures Data Warehouses, Data Lakes, and Data Lakehouses ETL and ELT procedures and Data Transformations Fundamentals of dbt (Data Build Tool) Analytics Engineering Modern Data Stack Slowly Changing Dimensions CTEs Once we understand the theoretical layer and how dbt fits into the picture, we will start building out a dbt project from scratch, just as you would do in the real world. Who is this course for? Data Engineers Data Analysts Data Scientists BI Developers BI Analyst ... and anyone who interacts with data lake/data warehouse/data lakehouse or uses SQL! Course Level Explained (Zero > Hero) The course has no 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 completing 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 will be next to you along the journey and you have our absolute support! When the Airbnb project is presented to you, you must do it entirely, 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 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. Toth dbt Mark and the dbt logo are trademarks of dbt Labs, Inc.
- 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
Temario
UNIDAD 1 - Course Introduction · 4 leccionesUNIDAD 1 - Course Introduction
UNIDAD 1 - Course Introduction
- What is dbt and Why should you use it?
- How can you take the most out of this course?
- ESSENTIAL READ ME - Course Resources
- How this Course Helps You Pass the Analytics Engineer Certification Exam
UNIDAD 2 - Building the first version of our Project · 11 leccionesUNIDAD 2 - Building the first version of our Project
UNIDAD 2 - Building the first version of our Project
- Use-case and Input Data Model Overview
- Snowflake Registration
- Snowflake's Authentication types and the Key-Pair Authentication
- Automatic Snowflake Data Import
- ONLY FOR REFERENCE - Snowflake Behind the Scenes
- dbt Roles, Users and Database Tables overview
- Installing dbt
- dbt Project Setup
- Say Hello to our dbt Project Folder
- Visual Studio Code Extensions Overview
- Datasets and Data Flow Overview
UNIDAD 3 - Models · 7 leccionesUNIDAD 3 - Models
UNIDAD 3 - Models
- Learning Objectives - Models
- Models Overview
- Theory: CTE - Common Table Expressions
- DeprecationWarning - a dbt bug
- Creating our first model: Airbnb listings
- Models Quiz
- Create the src_hosts model
UNIDAD 4 - Materializations · 8 leccionesUNIDAD 4 - Materializations
UNIDAD 4 - Materializations
- Learning Objectives - Materializations
- Materializations Overview
- Model Dependencies and dbt's ref tag
- Table type materialization & Project-level Materialization config
- Incremental materialization
- Incremental Strategies
- Ephemeral materialization
- Quiz - Materializations
UNIDAD 5 - Seeds and Sources · 5 leccionesUNIDAD 5 - Seeds and Sources
UNIDAD 5 - Seeds and Sources
- Learning Objectives - Seeds and Sources
- Seeds and Sources Overview
- Seeds
- Sources
- Source Freshness Checks
UNIDAD 6 - Snapshots · 6 leccionesUNIDAD 6 - Snapshots
UNIDAD 6 - Snapshots
- Learning Objectives - Snapshots
- Snapshots Overview
- Create a Snapshot
- Snapshots - Assignment Briefing
- Assignment: Create a Snapshot for the Hosts Source
- Snapshots Quiz
UNIDAD 7 - Tests · 11 leccionesUNIDAD 7 - Tests
UNIDAD 7 - Tests
- Learning objectives - Tests
- Tests Overview
- Generic Tests
- Create Generic Tests
- Debugging dbt Tests
- Saving Test Failures to the Data Warehouse
- Saving Test Failures into a Custom DB Schema
- Singular Tests
- Unit Tests
- Create your own singular test
- Tests Quiz
UNIDAD 8 - Advanced Testing: Contracts and Custom Generic Tests · 6 leccionesUNIDAD 8 - Advanced Testing: Contracts and Custom Generic Tests
UNIDAD 8 - Advanced Testing: Contracts and Custom Generic Tests
- Data Contracts
- Custom Generic Tests
- Custom Tests with Parameters
- Setting the Tests' Severity: Warning vs Error
- Add your tests to dim_hosts_cleansed
- dbt Constraints: Test at Insert Time
UNIDAD 9 - Jinja, Macros and Packages · 10 leccionesUNIDAD 9 - Jinja, Macros and Packages
UNIDAD 9 - Jinja, Macros and Packages
- Jinja Basics
- dbt fusion compatibilty
- Let's take Jinja for a Drive
- dbt-Specific Jinja Features
- Create Your Own Macros
- Advanced Jinja
- Advanced Macros in Action
- Assignment: Macros Output Formatting
- Installing Third-Party Packages
- Quick Macros Quiz
UNIDAD 10 - Documentation · 7 leccionesUNIDAD 10 - Documentation
UNIDAD 10 - Documentation
- Learning Objectives - Documentation
- Documentation Overview
- Writing and Exploring Basic Documentation
- Markdown-based Docs, Custom Overview Page and Assets
- The Linage Graph (Data Flow DAG)
- Document the dim_hosts_cleansed table
- Documentation quiz
UNIDAD 11 - Analyses, Hooks and Exposures · 11 leccionesUNIDAD 11 - Analyses, Hooks and Exposures
UNIDAD 11 - Analyses, Hooks and Exposures
- Learning Objectives - Analyses, Hook and Exposures
- Analyses
- Hooks
- Grants - Managing Permissions in dbt the Modern Way
- Nov 2025 - Snowflake Authentication Update
- USERNAME/PASSWORD VERSION - Setting up a BI Dashboard in Snowflake and Preset
- KEYPAIR - Say Hi to our BI Tool - preset.io
- KEYPAIR - Set up a BI Dashboard with our Final Tables
- READ ME - Exposures naming convention changes in recent dbt releases
- Exposures
- Hooks Quiz
UNIDAD 12 - dbt Hero · 1 leccionesUNIDAD 12 - dbt Hero
UNIDAD 12 - dbt Hero
- Welcome to Hero
UNIDAD 13 - Debugging Tests and Testing with dbt-expectations · 11 leccionesUNIDAD 13 - Debugging Tests and Testing with dbt-expectations
UNIDAD 13 - Debugging Tests and Testing with dbt-expectations
- A note on the dbt-expectations setup
- Annoucement for dbt 1.10+ users
- Great Expectations Overview
- Comparing row counts between models
- Looking for outliers in your data
- Implementing test warnings for extremal items
- Validating column types
- Monitoring categorical variables in the source data
- Debugging dbt tests and Working with regular expressions
- dbt-expectations and test debugging quiz
- Course Feedback and Moving on
UNIDAD 14 - Debugging with Logging · 4 leccionesUNIDAD 14 - Debugging with Logging
UNIDAD 14 - Debugging with Logging
- Logging to the dbt Log File
- Logging to the Screen
- Disabling Log Messages
- A short knowledge check
UNIDAD 15 - Debugging YAML, SQL, models and General dbt Bugs · 4 leccionesUNIDAD 15 - Debugging YAML, SQL, models and General dbt Bugs
UNIDAD 15 - Debugging YAML, SQL, models and General dbt Bugs
- An Overview of Advanced dbt Commands
- Taking Debugging Commands to Action
- Speeding up Development with --empty and Sampling (--sample)
- Using Flags
UNIDAD 16 - Tags and Selectors · 3 leccionesUNIDAD 16 - Tags and Selectors
UNIDAD 16 - Tags and Selectors
- Tags
- Model Selection Deep Dive
- YAML Selectors
UNIDAD 17 - Python Models · 5 leccionesUNIDAD 17 - Python Models
UNIDAD 17 - Python Models
- Python Models Overview
- Implementing a Simple Python Model
- Using Snowflake's Pandas-compatible connector
- Advanced Python Models
- Assignment - Create a Python Model to Extract Sentiment Scores
UNIDAD 18 - Using Variables · 6 leccionesUNIDAD 18 - Using Variables
UNIDAD 18 - Using Variables
- Working with Jinja Variables
- A note for Windows users using cmd
- Working with dbt Variables
- Setting Default Values
- Using Date Ranges to Make Incremental Models Production-Ready
- A quick quiz on variables
UNIDAD 19 - dbt in Production - Microbatching Incremental Models · 2 leccionesUNIDAD 19 - dbt in Production - Microbatching Incremental Models
UNIDAD 19 - dbt in Production - Microbatching Incremental Models
- Microbatching
- Excluding Models from Full Refresh
UNIDAD 20 - dbt in Production - Model Lifecycle - Versioning, Deprecating, Disabling Models · 4 leccionesUNIDAD 20 - dbt in Production - Model Lifecycle - Versioning, Deprecating, Disabling Models
UNIDAD 20 - dbt in Production - Model Lifecycle - Versioning, Deprecating, Disabling Models
- Model Lifecycle Introduction
- Model Versioning
- Deploying and Deprecating Versions
- Disabling Models
UNIDAD 21 - dbt in Production - Preparing a Project for Slim CI · 10 leccionesUNIDAD 21 - dbt in Production - Preparing a Project for Slim CI
UNIDAD 21 - dbt in Production - Preparing a Project for Slim CI
- Slim CI Introduction and Learning Objectives
- Working with Multiple Targets
- Using Environment Variables
- Setting Up Your Environment Variables for a Production-Ready Profile
- Custom Schemas: Separating Production and Development Environments
- Cleaning Up Development Schemas
- Retrying Failed Executions - --retry
- Comparing Production and Development State
- Using --defer for a Production-Ready Developer Experience
- A Case Study - How Bitrise manages 1200+ models in production
UNIDAD 22 - Implementing an End-to-End Slim-CI-Based Production System · 6 leccionesUNIDAD 22 - Implementing an End-to-End Slim-CI-Based Production System
UNIDAD 22 - Implementing an End-to-End Slim-CI-Based Production System
- Slim CI Workflow Overview
- Integration and Deployment Using Github Actions
- GitHub Secrets - Storing Credentials Sensitive Data
- Production Pipelines and Artifacts
- Creating and Testing Feature Branches and Integrating to Production
- Automized Production Deployment and Development Environment Cleanup
UNIDAD 23 - dbt Fusion and the Official Visual Studio Code Extension · 15 leccionesUNIDAD 23 - dbt Fusion and the Official Visual Studio Code Extension
UNIDAD 23 - dbt Fusion and the Official Visual Studio Code Extension
- dbt Fusion - Introduction
- Exploring the Technical Differences Between dbt Fusion and dbt Core
- dbt Fusion Feature Matrix
- Licence Changes
- Instructions if you want to use our Plug-and-Play CodeSpace
- dbt Fusion and the VSCode Extension Installation and Walkthrough
- dbt Fusion Installation for Production Systems
- Running dbt Core and dbt Fusion side by side
- Upgrade your Projects to Fusion and dbt 1.10+ using dbt-autofix
- Building our Project with Fusion
- Official VSCode Extension - Development Workflow & Execution
- Official VSCode Extension - Navigation & Code Intelligence Features
- Official VSCode Extension - Project and Column-Level Lineage
- Upgrading Fusion with the dbtf System Utility
- dbt Fusion Quiz
UNIDAD 24 - Orchestrating dbt with Dagster · 7 leccionesUNIDAD 24 - Orchestrating dbt with Dagster
UNIDAD 24 - Orchestrating dbt with Dagster
- Overview of the Popular dbt Orchestration Tools and how to Choose the Right Tool
- A Note on Installing Dagster
- Creating a Dagster project and connecting it to our dbt Project
- Dagster dbt files and UI Overview
- Manage, Orchestrate and Debug your dbt Project with Dagster
- A Note on the Advanced Dagster Section
- Advanced Dagster: Using Partitions with Incremental Models
UNIDAD 25 - Capstone Project · 2 leccionesUNIDAD 25 - Capstone Project
UNIDAD 25 - Capstone Project
- Capstone Project Briefing
- Capstone Project Instructions
UNIDAD 26 - An alternative VSCode Extension - Power User for dbt Core · 15 leccionesUNIDAD 26 - An alternative VSCode Extension - Power User for dbt Core
UNIDAD 26 - An alternative VSCode Extension - Power User for dbt Core
- dbt Power User - Extension and Codespace Introduction
- dbt Power User Overview
- dbt Power User - Setup
- Sign Up for a Free Account
- dbt Power User - Working with and Fixing Legacy Code
- dbt Power User - Review Existing Foundation
- The Prompt we use for AI Model Building
- dbt Power User - Building Models with AI
- dbt Power User - Compiling and Running Models
- dbt Power User - Use AI to Generate Documentation
- dbt Power User - Creating Data Quality Tests
- dbt Power User - Project Health Check
- dbt Power User - Recap
- Altimate Code: A Coding Agent for dbt
- dbt Power User - What's Next?
UNIDAD 27 - Best Practices for Introducing and Using dbt in your Company · 1 leccionesUNIDAD 27 - Best Practices for Introducing and Using dbt in your Company
UNIDAD 27 - Best Practices for Introducing and Using dbt in your Company
- An interview with the Data Utilization Head of the Vienna Insurance Group
UNIDAD 28 - dbt Certification Exam Preparation Guide · 4 leccionesUNIDAD 28 - dbt Certification Exam Preparation Guide
UNIDAD 28 - dbt Certification Exam Preparation Guide
- Guide and Resources to the Certification Preparation Section
- How to prepare for the certification exam? An interview with Muizz Lateef
- 2026 Analytics Engineer Certification Reflection and Preparation Guide
- Knowledge Check / dbt Analytics Engineer Certification Exam Sample
UNIDAD 29 - REFERENCE - Theory 1 - The Data Maturity Model · 4 leccionesUNIDAD 29 - REFERENCE - Theory 1 - The Data Maturity Model
UNIDAD 29 - REFERENCE - Theory 1 - The Data Maturity Model
- Introduction - Maslow's Pyramid of Data
- The Data Maturity Model
- ETL and ELT
- The Data Maturity Model
UNIDAD 30 - REFERENCE - Theory 2 - Data Warehouses, Data Lakes and Lakehouses · 5 leccionesUNIDAD 30 - REFERENCE - Theory 2 - Data Warehouses, Data Lakes and Lakehouses
UNIDAD 30 - REFERENCE - Theory 2 - Data Warehouses, Data Lakes and Lakehouses
- Data Warehousing - a short introduction
- External Tables and Cloud Data Warehouses
- Data Lakes
- Data Lakehouse
- Data Warehouses, Data Lakes and Lakehouses
UNIDAD 31 - REFERENCE - Theory 3 - The Modern Data Stack · 2 leccionesUNIDAD 31 - REFERENCE - Theory 3 - The Modern Data Stack
UNIDAD 31 - REFERENCE - Theory 3 - The Modern Data Stack
- The Modern Data Stack
- The Modern Data Stack
UNIDAD 32 - REFERENCE - Theory 4 - Slowly Changing Dimension (SCD) · 6 leccionesUNIDAD 32 - REFERENCE - Theory 4 - Slowly Changing Dimension (SCD)
UNIDAD 32 - REFERENCE - Theory 4 - Slowly Changing Dimension (SCD)
- The Basics of Slowly Changing Dimensions
- Type 0 - Retain Original
- Type 1 - Overwrite
- Type 2 / SCD2 - Add New Row
- Type 3 - Add New Attribute
- Slowly Changing Dimension (SCD)
UNIDAD 33 - REFERENCE - Python Installation · 2 leccionesUNIDAD 33 - REFERENCE - Python Installation
UNIDAD 33 - REFERENCE - Python Installation
- Which Python Version should I install?
- OPTIONAL - WINDOWS - Installing Python and pip
UNIDAD 34 - Bonus Lecture · 1 leccionesUNIDAD 34 - Bonus Lecture
UNIDAD 34 - Bonus Lecture
- Bonus Lecture
Tu certificado lo emite la SEP, no solo una plataforma
Al demostrar la competencia recibes un certificado emitido por la Secretaría de Educación Pública a través del Gobierno del Estado de México: un documento oficial que puedes presentar con confianza ante cualquier empleador o institución.
Tu certificado con INEC
- Lo emite la Secretaría de Educación Pública (SEP), a través del Gobierno del Estado de México
- Documento oficial con folio verificable en línea
- Lo otorga la Universidad Tecnológica Fidel Velázquez
Otras plataformas en línea
- Constancia privada emitida por la misma plataforma
- Solo acredita horas cursadas o asistencia
- Sin respaldo de una autoridad educativa
Tu itinerario, de la inscripción al certificado
- 1
Inscripción con pago único
Pagas $2,800 MXN una sola vez y entras de inmediato al curso.
- 2
Estudias a tu ritmo
34 unidades con videos, lecturas y actividades, disponibles 24/7 durante 12 meses.
- 3
Cuestionarios por unidad
26 cuestionarios para comprobar lo aprendido.
- 4
Certificado
Emitido por la SEP a través del Gobierno del Estado de México, con folio verificable.
Un certificado oficial de la SEP
- Emitido por la Secretaría de Educación Pública (SEP), a través del Gobierno del Estado de México. No es una constancia privada: es un documento oficial, y ese respaldo marca la diferencia frente a otras plataformas en línea.
- Contenido impartido por Zoltan C. Toth.
- Folio y QR verificables para compartirlo con quien te contrate.
Empieza hoy, a tu ritmo
Un solo pago y acceso inmediato al curso completo durante 12 meses.
- 34 unidades · 206 lecciones · 60 h
- 12 meses de acceso
- 26 cuestionarios de evaluación
- Certificado emitido por la SEP (Gob. del Estado de México) con folio verificable
Sobre tu inscripción
¿Cómo es el pago?
Un solo pago de $2,800 MXN. No hay mensualidades ni cargos recurrentes.
¿Cuánto tiempo tengo acceso?
12 meses desde tu inscripción, 24/7, para avanzar a tu ritmo.
¿Quién imparte el contenido?
El curso es impartido por Zoltan C. Toth; INEC gestiona tu inscripción, evaluación y certificado.
¿Qué pasa cuando vence mi acceso?
Puedes renovar tu cuenta tú mismo desde la plataforma, sin trámites ni esperas.


