Building a Dimensional Data Warehouse using dbt and BigQuery.

This repository contains the Use Case of a Dimensional DWH build using dbt and bigquery, it contains two folders:

1. The folder DL_NORTHWIND contains the dataset to model on dbt

2. the other folder is the name assigned to the dbt project, in this case NT_DWH

0. Install pyenv to manage Python versions and virtual environments: https://github.com/pyenv/pyenv

1. Create a new Python environment for the dbt and its big query adapter:
   

    pyenv virtualenv dbt-bigquery
   

2. Install dbt-core and dbt-bigquery:
   

    pip install --upgrade pip
    pip install dbt-core
    pip install dbt-bigquery
   

   and check the version:
   

    dbt --version
   

   that should be the output message:
   

3. Initialize the dbt project and assign it a valid name. In this use case, the name is "NT_DWH" as per Northwind Traders Data Warehouse:
   

   • run dbt init NT_DWH
   • choose bigquery as database type
   • choose oauth as authenthication method
   • add your GCP project_id

4. Now execute dbt debug and complete the configuration of your profiles.yml with the connections to the Database

    ~/.dbt/profiles.yml
        my-bigquery-db:
            target: dev
            outputs:
                dev:
                type: bigquery
                method: oauth
                project: [GCP project id]
                dataset: [the name of your dbt dataset] # You can also use "schema" here
                threads: [1 or more, number of parallelism in the tasks execution]
                <optional_config>: <value>
   

5. Setup the gcp oauth using gcloud: https://docs.getdbt.com/docs/core/connect-data-platform/bigquery-setup#local-oauth-gcloud-setup:

    gcloud auth application-default login \
    --scopes=https://www.googleapis.com/auth/bigquery,\
    https://www.googleapis.com/auth/drive.readonly,\
    https://www.googleapis.com/auth/iam.test
   

6. If you work with multiple warehouses you need to tell dbt which profiles.yml to use. There are several ways to do that, for more info check the official documentation: https://docs.getdbt.com/docs/core/connect-data-platform/connection-profiles.
   One possibility is to use the the --profiles-dir flag every time you execute a dbt command, followed by 'path/to/directory'.
   

That's how a well configured dbt project, looks like: