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dbt container skeleton

This project is intended to be used as a way to bootstrap a containerized dbt full development environment.

Features

This respository has several features to help get a new dbt project started, or enhance an existing one, including:

  • A containerized development environment -- Simply run inv dbt-shell and run all your dbt commands, knowing that everyone else on your team is running the same version of dbt and dependencies.
  • dtspec testing -- The dbt shell environment comes installed with the dtspec testing package and the CLI tools needed to make it work easily with dbt.
  • SQLfluff -- The dbt shell also comes with SQLfluff, which will lint your dbt SQL code. This ensures that all of your team is following the same code style best practices. No more internal wars over SQL formatting!
  • GitHub Actions -- This project comes with a basic github action workflow (in .github/workflows). This workflow will use GitHub Actions to lint and test your code every time it is changed and pushed to GitHub. Modify as you see fit to build out your own CI/CD processes.
  • Invoke task execution for automating various project tasks using Python.

Setup

First, you'll need to download and install docker community edition. It is strongly recommended that you install a Python environment manager like miniconda. After installing miniconda, create a minconda environment for this project via

conda create --name dbt-container-skeleton python=3.8

Then activate the environment (this will need to be done every time you open a new shell):

conda activate dbt-container-skeleton

Next, install the Python packages needed to manage your dbt container via

pip install -r host_requirements.txt

Build the dbt container via

inv build

The example dbt project included assumes there is a Postgres database running on your host machine. To configure the credentials needed to connect to this database run

inv config

This will ask you for your Postgres username and password and allows you to customize the host and database name as well

POSTGRES_HOST=host.docker.internal # Use this for a local database running on the host
POSTGRES_USER=<yourname>
POSTGRES_PASSWORD=<yourpassword>
POSTGRES_DBNAME=dbt_container_skeleton # You will have to create this Postgres database

Lastly, open up a shell in the dbt container and try running the example dbt project

inv dbt-shell
dbt seed
dbt run

The container that runs mounts your local repository into the container, so whenever you modify code on your host machine, it will be immediately available in the container.

dtspec

This project also supports running dtspec tests. To run those, first create a test database on your host's postgres server named <POSTGRES_DBNAME>_dtspec and initialize the database via (in the dbt-shell)

dtspec db --init-test-db

Then run the dtspec tests with:

dtspec dtspec-test-dbt

SQLFluff

In the dbt shell continer, you can also use SQLfluff to lint your dbt SQL code. Simply run

sqlfluff lint models

The SQLFluff configuration file is found in dbt/.sqlfluff.

Use

Next steps for your project

After going through the setup exercises, you'll want to start customizing this project for your dbt needs. If you already have a dbt project, you should be able to just copy your project into the dbt directory. One exception to this involves your dbt profiles.yml file. That file also needs to be placed in the dbt directory. However, you shouldn't commit this file to version control if it contains any secrets. Instead, all of the secrets in your existing profiles.yml file should be replaced by environment variables that get set via the inv config command mentioned above. Examine the dbt/profiles.yml file in this project to how it is used.

If you need to add or modify the environment variables that get set in the container, you only need to edit the config_template.env file. This file also should not contain any secrets, as it merely lists out the customizable environment variables with some non-secret defaults. After modifying this file, rerun inv config to set any secrets.

A note about secrets

When you run inv config for the first time, a secret key is generated and stored on your host machine at ~/.dbt-container-skeleton. This is a key used to encrypt and decrypt your secrets, which get stored in .config.env (also excluded from version control). If you lose the secret key, you will be unable to decrypt this file and will have to rerun inv config. This ensures that secrets are not stored in plaintext on your host machine.

If you need to view the secrets in plaintext, you may do show with

inv config-show

Invoke

All of the inv commands are run via invoke tasks defined in tasks.py. To see a list of available commads, run

inv -l

Upgrading/adding python packages

You will want to make sure that the python packages used by this project are kept up to date. There is a handy invoke task configured to upgrade these packages (using pip-tools) by running:

inv requirements-upgrade

This will upgrade all of the packages listed in requirements.in and their dependencies.

If you ever need to add a Python package to this project, add it to the requirements.in file and run

inv requirements-compile

This will add the new package to the project, but will not upgrade the other packages listed in requirements.in.

When to build?

Your host dbt folder is mounted within the container, so you do not need to rebuild the container every time you change a file. However, whenever you need to modify a Python or dbt package, you'll need to run inv build afterwards.

For example, if you needed to downgrade dbt to version 0.18.1, You would modify the requirements.in file and change the dbt line to dbt==0.18.1. Then compile the requirements to resolve package dependencies via

inv requirements-compile

If all is successful (no package conflicts), rebuild the container with inv build.