complete-dbt-bootcamp-zero-to-hero: the dbt courseware behind a Udemy course

This is courseware for a paid course, not a dbt package

The repository description is blunt about what it holds: the working files that support a Udemy course. The course notes open by pointing at the course website on Udemy and describing a course that is sold there. Nothing in the repository is packaged for installation as a dbt library, and there is no published package on any registry.

That shapes how the artifacts should be read. The Python in pyproject.toml is the environment for running dbt and Dagster while working through the exercises, not an API other projects import. The airbnb/ and dbt_dagster_project/ directories are the worked example that the course walks through, so their structure follows teaching order rather than a general purpose layout.

Who it serves is therefore narrower than the topic. The course targets data engineers, data analysts, data scientists, BI developers and BI analysts, and adds anyone who interacts with a data lake, data warehouse or data lakehouse or who uses SQL. The repository is the supporting half of a paid course, and it assumes the exercises are done in sequence, which the course notes state explicitly: every exercise is an unavoidable step, and starting a harder one before finishing the earlier ones leads to difficulty.

What the top level of the repository actually contains

The top level entry list is short and each directory has a job. There is airbnb/, the dbt project built through the practical section of the course. There is dbt_dagster_project/, a second version of the same work with orchestration. There is _course_resources/, holding supporting course assets. There is a .devcontainer/ directory, which is the containerised development environment the exercises expect, and a .dockerignore alongside it.

The rest is tooling and metadata: pyproject.toml at the root, a requirements.txt kept for reference, a uv.lock, a .github/ directory, .gitignore, LICENSE.md and README.md.

Two things are worth flagging for anyone planning to copy from this repo. First, the repository is a snapshot of a teaching artifact, so directory names follow the course's narrative rather than a convention you would pick yourself. Second, the presence of both an airbnb/ and a dbt_dagster_project/ means you will encounter the same models twice in two shapes, and picking which one to read is a decision the course notes do not make for you.

Two manifests with two different pin sets

Dependency management here is split across files that do not agree exactly, and the repository says so. The requirements.txt opens with two comment lines: the file is kept for reference only, and the uv-based pyproject.toml file is the one to use for installing dependencies. That is an explicit instruction, and it settles the question of which file wins.

The pyproject.toml declares the project as dbt-bootcamp version 1.11.0, requires Python >=3.10,<3.14, and pins the dependency set:

[project]
name = "dbt-bootcamp"
version = "1.11.0"
requires-python = ">=3.10,<3.14"
dependencies = [
    "dbt-core~=1.11.0",
    "dbt-snowflake~=1.11.0",
    "dbt-autofix~=0.18.0",
    "dagster-dbt~=0.28.0",
    "dagster-webserver~=1.12.0",
]

The reference file pins exact versions instead, and the two do not line up. pyproject.toml allows dbt-autofix at 0.18.0 while requirements.txt records 0.21.3, and the Dagster packages sit at 0.28.0 and 1.12.0 in the project file but at 0.29.13 and 1.13.13 in the reference file. The dbt-core and dbt-snowflake lines are closer, with 1.11.0 ranges against 1.11.12 and 1.11.6 exact pins.

The practical consequence is that two people installing this repository can end up with different tooling, and nothing in the files tells you which combination the course exercises were written against. The Python floor is the other constraint worth knowing: dbt 1.11 with Python 3.13 or 3.14 is outside the declared range, so a machine on a newer interpreter is out of scope for these pins.

The Airbnb project is the spine of the curriculum

The teaching method is a single running project rather than isolated snippets. The course notes say the practical section covers each dbt feature present at the time through the construction of a complete, real-world project, and names that project as Airbnb. The stated purpose is to show which features should be used at what stage of a given project.

The feature list is broad enough to make the point: models, table and incremental build types, tests, documentation, sources, seeds, snapshots, hooks and operations, Jinja and macros, analyses and exposures, and data visualization with Preset. The environment setup comes first, with MAC and Windows setup paths, IDE dbt extension installation, creation and activation of virtual environments, and Snowflake setup.

Note what that implies about prerequisites. Snowflake is the warehouse, and the exercise set is written against a live Snowflake account rather than a local mock, so following the repository end to end needs warehouse credentials and a paid or trial account before any dbt command can be interesting. The theory section is the other half: data maturity models, data architectures, warehouses, lakes and lakehouses, ETL and ELT, normalization, slowly changing dimensions and CTEs.

Why Dagster appears alongside dbt

The dbt_dagster_project/ directory and the dagster-dbt and dagster-webserver dependencies point at a second layer that a plain dbt project would not have. dbt itself transforms data inside the warehouse; Dagster is an orchestrator that decides what runs when, and can trigger dbt models as steps of a wider pipeline.

The repository does not spell out the integration in the files shown here, but the dependency set makes the shape clear enough to reason about. dagster-dbt is the adapter that lets Dagster run and observe dbt projects, and dagster-webserver is the local UI where that run history is inspected. Splitting the repository into airbnb/ and dbt_dagster_project/ is the natural way to let the same models appear once as a plain dbt project and once driven by a scheduler.

This is the part of the repository that generalizes beyond the course. A standalone dbt project is not scheduled by itself, so on anything beyond a laptop you eventually need an orchestrator, and here the pairing is already built. The trade-off is added complexity: a second configuration surface, another service to run locally, and a failure mode where a dbt model is fine but its orchestration entry is not.

Setting up the environment from pyproject.toml

There is no install script in the repository. The route to a working environment is the uv-based pyproject.toml, and the requirements.txt file is explicitly reference only, so ignore its pins when installing. The .devcontainer/ directory is the other option, for working inside a container rather than on the host.

The order of work follows the course structure: create and activate a virtual environment, install the pyproject.toml dependencies with uv, point the dbt project at a Snowflake account, then run the models. The setup notes also expect the IDE dbt extension to be installed for authoring, which matters for reading the exercise files with syntax highlighting and model navigation.

Two practical cautions. The declared Python range is >=3.10,<3.14, so pick an interpreter inside it before creating the virtual environment rather than discovering the mismatch after a failed install. And since the reference file disagrees with the project file on dbt-autofix and the Dagster versions, install from pyproject.toml alone and do not mix the two, or you will end up with a combination the course never tested.

A custom licence and a single old release tag

The licence is the first thing to resolve before copying code from this repository. The classification GitHub applies is one it cannot categorise, which points at a custom file rather than a standard permissive licence, and LICENSE.md at the repository root is where the actual terms live. The licence file itself is where the terms sit, and the course notes do not summarise them, so anyone planning to reuse the dbt models or the Dagster project in commercial work needs to read that file directly rather than infer terms from the dbt ecosystem defaults.

The repository is not archived and the last push was on 2026-10-05, so the files are still being touched. The release history tells a different story: there is a single tag, 1.3.0, named "Courseware for dbt from 1.0.0 to 1.3.0", dated 2023-02-02. The root pyproject.toml, meanwhile, is at version 1.11.0 and pins dbt-core 1.11.x.

Those two version numbers mean different things, and confusing them is the main trap in this repository. The tag describes the span of dbt versions the courseware covered at the time of release, while the project version tracks the dbt line the environment is currently pinned to. If you need to know which dbt release a set of models was written against, the tag list will not tell you, and the CHANGELOG.md at the repository root is the place to look.

Editorial conclusion

Treat this repository as courseware for a paid Udemy course rather than as a reusable dbt project or a library to depend on. It is worth reading for the airbnb/ and dbt_dagster_project/ examples if you are learning dbt, but there is no published package, no versioned API and no upstream maintenance commitment. Before adopting anything from it, check LICENSE.md yourself, because the licence classification GitHub applies is a custom one that the file itself defines, and confirm that the dbt 1.11 pins in pyproject.toml match the dbt version your own warehouse supports.

Frequently asked questions

Official sources

1. Issues
2. README
3. Releases
4. zoltanctoth/complete-dbt-bootcamp-zero-to-hero on GitHub