Foundational structure

In this step, we’ll need to create a development branch and set up project level configurations.

To get started with development for our project, we'll need to create a new Git branch for our work. Select create branch and name your development branch. We'll call our branch snowpark_python_workshop then click Submit.
The first piece of development we'll do on the project is to update the dbt_project.yml file. Every dbt project requires a dbt_project.yml file — this is how dbt knows a directory is a dbt project. The dbt_project.yml file also contains important information that tells dbt how to operate on your project.

Select the dbt_project.yml file from the file tree to open it and replace all of the existing contents with the following code below. When you're done, save the file by clicking save. You can also use the Command-S or Control-S shortcut from here on out.

# Name your project! Project names should contain only lowercase characters
# and underscores. A good package name should reflect your organization's
# name or the intended use of these models
name: 'snowflake_dbt_python_formula1'
version: '1.3.0'
require-dbt-version: '>=1.3.0'
config-version: 2

# This setting configures which "profile" dbt uses for this project.
profile: 'default'

# These configurations specify where dbt should look for different types of files.
# The `model-paths` config, for example, states that models in this project can be
# found in the "models/" directory. You probably won't need to change these!
model-paths: ["models"]
analysis-paths: ["analyses"]
test-paths: ["tests"]
seed-paths: ["seeds"]
macro-paths: ["macros"]
snapshot-paths: ["snapshots"]

target-path: "target"  # directory which will store compiled SQL files
clean-targets:         # directories to be removed by `dbt clean`
 - "target"
 - "dbt_packages"

models:
 snowflake_dbt_python_formula1:
   staging:

 +docs:
   node_color: "CadetBlue"
marts:
 +materialized: table
 aggregates:
   +docs:
     node_color: "Maroon"
   +tags: "bi"

 core:
   +docs:
     node_color: "#800080"
 intermediate:
   +docs:
     node_color: "MediumSlateBlue"
 ml:
   prep:
     +docs:
       node_color: "Indigo"
   train_predict:
     +docs:
       node_color: "#36454f"

The key configurations to point out in the file with relation to the work that we're going to do are in the models section.
- require-dbt-version — Tells dbt which version of dbt to use for your project. We are requiring 1.3.0 and any newer version to run python models and node colors.
- materialized — Tells dbt how to materialize models when compiling the code before it pushes it down to Snowflake. All models in the marts folder will be built as tables.
- tags — Applies tags at a directory level to all models. All models in the aggregates folder will be tagged as bi (abbreviation for business intelligence).
- docs — Specifies the node_color either by the plain color name or a hex value.
Materializations are strategies for persisting dbt models in a warehouse, with tables and views being the most commonly utilized types. By default, all dbt models are materialized as views and other materialization types can be configured in the dbt_project.yml file or in a model itself. It’s very important to note Python models can only be materialized as tables or incremental models. Since all our Python models exist under marts, the following portion of our dbt_project.yml ensures no errors will occur when we run our Python models. Starting with dbt version 1.4, Python files will automatically get materialized as tables even if not explicitly specified.
```
marts:     
  +materialized: table
```