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https://github.com/gnilrets/dbt-container-skeleton

All the basics to get a nice containerized dbt development environment
https://github.com/gnilrets/dbt-container-skeleton

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All the basics to get a nice containerized dbt development environment

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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](https://github.com/inside-track/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](https://docs.sqlfluff.com/en/stable/) -- 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](https://docs.github.com/en/actions) -- This project comes with

  a basic github action workflow (in [.github/workflows](.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](http://www.pyinvoke.org/) task execution for automating various

  [project tasks](tasks.py) using Python.



## Setup



First, you'll need to download and install [docker community

edition](https://www.docker.com/).  It is strongly recommended that you

install a Python environment manager like

[miniconda](https://conda.io/miniconda.html).  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=

    POSTGRES_PASSWORD=

    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](https://github.com/inside-track/dtspec) tests.

To run those, first create a test database on your host's postgres server named

`_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](https://docs.sqlfluff.com/en/stable/)

to lint your dbt SQL code.  Simply run



    sqlfluff lint models



The SQLFluff configuration file is found in [dbt/.sqlfluff](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](http://www.pyinvoke.org/) 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](https://github.com/jazzband/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`.