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https://github.com/slve/dbt-github-workflow

dbt-github-workflow is a boilerplate that contains all the necessary configurations to set up a simple CI/CD pipeline for your data modelling stack, making your life simpler by adding back a few extra working hours / out of hours
https://github.com/slve/dbt-github-workflow

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dbt-github-workflow is a boilerplate that contains all the necessary configurations to set up a simple CI/CD pipeline for your data modelling stack, making your life simpler by adding back a few extra working hours / out of hours

Lists

README 

        
Intro

-----

dbt-github-workflow is a boilerplate

that contains all the necessary configurations

to set up a simple CI/CD pipeline for your

data modelling stack, making your life simpler

by adding back a few extra working hours / out of hours.



I will also add some more resources to this project,

feel free to star/follow.



What it does?

-------------

Each time you create a new PR or push to the main branch

the github continuous integration

at .github/workflows/continuous-integration.yml

will

- setup python

- configure a cache to speed up future python package installs

- install the python requirements specified in the requirements.txt

- instatiate the dbt profile by copying the project profiles.yml

  to the github runner home ~/.dbt/ folder

- instantiate the keyfile

- builds the project using `make build `

  - runs pre-commit through on changed files

  - runs sqlfluff on the changed files

    (not in pre-commit, for more readable output)

- authenticates on GCS

- deploys the dbt project to composer's dags/dbt folder



Contribute

----------

While this setup is using

- GCP

- BigQuery

- Airflow in GCP Composer



is not tied to it, you can easily amend it to your needs.

Feel free to create a public fork and share back the part

of your setup that can be shared.



Setup

-----

1. Private fork or copy the content of this repository to your project

2. Update the profiles.yml

3. Setup the following environment variables

   - KEYFILE_DIR - where your keyfile.json will get stored

   - GCLOUD_PROJECT - the name of your GCP project

4. Copy/Symlink/Instantiate your keyfile.json,

   it's content is a service account keyfile

   that has at least the following roles in GCP IAM

   - BigQuery Data Owner - for dbt so that it can make changes

   - BigQuery Job User - for dbt so it can run queries

   - BigQuery Read Session User - for dbt so it can create and read sessions

   - Storage Object Admin - in order to be able to upload the dbt project content to Airflow's Composer GCS bucket

5. Setup the following github secrets:

   - SERVICE_ACCOUNT_KEY - contains the actual value of your keyfile.json

   - COMPOSER_BUCKET - contains the bucket id as in gs://BUCKET_ID/dags/

     which belongs to the DAGs folder of your very environment setup in GCP Composer

6. Configure your github project in case you would like to protect the main branch

   at https://github.com/USER/PROJECT/settings/branches

   add a new branch protection rule and configure it to your needs