Getting Started with Analytics Engineering

Getting Started

To start the Docker compose:

make up

To set up and activate Python virtual environment

python -m venv ENV
source ENV/bin/activate
pip install -r requirements.txt

To initialize a dbt project

dbt init

Note: Let's specify the project name greenery.

To set up your dbt profile

Change the directory to your dbt project.
```
cd <dbt_project_name>
```
Run the following command to copy the profiles example file to the real profiles file.
```
cp profiles.yml.example profiles.yml
```

Edit the content int the profiles file by changing the output and target to your name (e.g., dbt_john), and save. See the example below.

greenery:

  outputs:
    dbt_zkan:
      type: postgres
      threads: 1
      host: localhost
      port: 5432
      user: postgres
      pass: "{{ env_var('DBT_ENV_SECRET_PG_PASSWORD') }}"
      dbname: greenery
      schema: dbt_zkan

    prod:
      type: postgres
      threads: 1
      host: localhost
      port: 5432
      user: postgres
      pass: "{{ env_var('DBT_ENV_SECRET_PG_PASSWORD') }}"
      dbname: greenery
      schema: prod

  target: dbt_zkan

Set the environment variable.

export DBT_ENV_SECRET_PG_PASSWORD=postgres

To debug the dbt project

export DBT_ENV_SECRET_PG_PASSWORD=postgres
cd <dbt_project_name>
dbt debug

To create your data models

export DBT_ENV_SECRET_PG_PASSWORD=postgres
cd <dbt_project_name>
dbt run

To test your data models

export DBT_ENV_SECRET_PG_PASSWORD=postgres
cd <dbt_project_name>
dbt test

firstnapat/getting-started-with-analytics-engineering