dbt Training - Rapid Onboarding Exemplar

This is a dbt project for dbt Lab's Rapid Onboarding training.

Our analytics stack:

• Loader: Snowflake's TPCH sample data
• Warehouse: Snowflake
• Transformation: dbt

Permissions

Access to the Snowflake warehouse is managed on a per-user basis by the training team. If you need access, open a request in the #training-rapid-onboarding Slack channel.

Using This Project

Developing in the Cloud IDE

The easiest way to contribute to this project is by developing in dbt Cloud. If you need access, contact the training team in the #training-rapid-onboarding Slack channel.

Once you have access, navigate to the develop tab in the menu and fill out any required information to get connected.

In the command line bar at the bottom of the interface, run the following commands one at a time:

• dbt deps - installs any packages defined in the packages.yml file.
• dbt seed - builds any .csv files as tables in the warehouse. These are located in the data folder of the project.
• dbt run - builds the models found in the project into your dev schema in the warehouse.

Local Development

1. Install Requirements

   Install dbt.
   Optionally, you can set up venv to allow for environment switching.

2. Setup

   Open your terminal and navigate to your profiles.yml. This is in the .dbt hidden folder on your computer, located in your home directory.

   On macOS, you can open the file from your terminal similar to this (which is using the Atom text editor to open the file):

   $ atom ~/.dbt/profiles.yml

   Insert the following into your profiles.yml file and change out the bracketed lines with your own information. Here is further documentation for setting up your profile.

   my_project:                                          
    target: dev                                         
    outputs:                 
      dev:                                              
        type: [warehouse name]                                 
        threads: 8                                      
        account: [abc12345.us-west-1]                   
        user: [your_username]                           
        password: [your_password]                       
        role: transformer                               
        database: analytics                             
        warehouse: transforming                         
        schema: dbt_[your_name]                         

   Configuration Key	Definition
   my_project	This is defining a profile - this specific name should be the profile that is referenced in our dbt_project.yml
   target: dev	This is the default environment that will be used during our runs.
   outputs:	This is a prompt to start defining targets and their configurations. You likely won't need more than dev, but this and any other targets you define can be used to accomplish certain functionalities throughout dbt.
   dev:	This is defining a target named dev.
   type: [warehouse_name]	This is the type of target connection we are using, based on our warehouse.
   threads: 8	This is the amount of concurrent models that can run against our warehouse, for this user, at one time when conducting a dbt run
   account: [abc12345.us-west-1]	Change this out to the warehouse's account.
   user: [your_username]	Change this to use your own username that you use to log in to the warehouse
   password: [your_password]	Change this to use your own password for the warehouse
   role: transformer	This is the role that has the correct permissions for working in this project.
   database: analytics	This is the database name where our models will build
   schema: dbt_[your_name]	Change this to a custom name. Follow the convention dbt_[first initial][last_name]. This is the schema that models will build into / test from when conducting runs locally.

3. Running dbt

   Run the following commands one at a time from your command line:

   • dbt debug - tests your connection. If this fails, check your profiles.yml.
   • dbt deps - installs any packages defined in the packages.yml file.
   • dbt seed - builds any .csv files as tables in the warehouse. These are located in the data folder of the project.
   • dbt run - builds the models found in the project into your dev schema in the warehouse.