So you know what dbt is, you have some data in BigQuery and your keyboard is beckoning you to type some SQL.
In this post, I will be showing you how to install and run dbt from your local machine from the command-line interface (CLI). This is also called dbt Core. In addition to this, you may run dbt from a web-based application called dbt Cloud. I won’t be showing that here.
Let’s get you started.
First, create a project or connect to your existing project on BQ (BigQuery) by:
- heading go over to the BQ console →
- creating a new project 👇
You should have a fresh new project now; looking something like this 👇
Great! Now let’s get you connected to the project. You have a few options here, which dbt kindly lists here. I will show you how to implement OAuth via google’s gcloud CLI tool.
Setup OAuth Profile & gcloud Configuration
First, make sure that your account has the proper IAM permissions for BigQuery access.
Note: If you created the project in the previous steps you should be good to go with permissions implicitly. The following step will explicitly add permissions.
Navigate to menu → IAM & Admin → IAM. Once here, add BigQuery Admin to your account’s role.
Now, install google-cloud-sdk
brew install --cask google-cloud-sdk
Once dbt is installed you will need to set up a profile to connect to your BigQuery projects. The following is an example of the setup for one project.
target: dev" >> ~/.dbt/profiles.yml
log in to your google account via gcloud
gcloud config configurations create <profile_name>
gcloud config set project <google-project-name>
gcloud auth application-default login
🎉 Now you’ve got a BigQuery instance ready for all kinds of dbt’ing.
dbt is, under the hood, a python package so you are going to need a few things before you can get it to run. Assuming you are on a mac, you can get everything you need via the following commands:
brew install git
brew tap fishtown-analytics/dbt
brew install dbt
To test your installation, run
Finally, you are ready to run
dbt init <your_project_name>
which will generate a skeleton for your project in the directory you run it in.
There you have it. You’re ready to go create, test, and document your data in BigQuery through dbt.
Feel free to follow along with the CLI suggestions (seen in the previous image and/or your terminal) to get up and running or check out my next post in which I will go through the exact commands needed for said creating, testing, and documenting.
P.S. Want to set up on a warehouse other than BigQuery? Say Redshift or Snowflake? Message me here and let me know which you would like another post around. Or, if you just have to get your dbt on right now, message me and I can run you through it 👌
If you’d like to hop on a call to discuss your data warehouse with a Flywheel engineer, just shoot us a note at firstname.lastname@example.org.