dbt YAML Configuration File

A dbt YAML Configuration File is a YAML configuration file for a dbt-based system.

• See: ....

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References

2023

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  • Data Model Configuration: The data model configuration is used to define the schema for a particular data model. Here's an example of what a data model configuration might look like:

models:
 - name: orders
   description: "A table of orders and their associated metadata."
   columns:
     - name: order_id
       description: "The unique identifier for the order."
       tests:
         - unique
     - name: order_date
       description: "The date the order was placed."
       tests:
         - not_null
     - name: customer_id
       description: "The unique identifier for the customer who placed the order."
       tests:
         - not_null

• In this example, we're defining a data model called "orders" and specifying the schema for that table. We're also defining some tests that will be run to ensure the quality of the data in the table.
  
  
  
  • Macro Configuration: The macro configuration is used to define macros that can be used in DBT projects. Here's an example of what a macro configuration might look like:

macros:
 - name: calculate_discount
   description: "Calculates the discount for an order based on the total amount."
   arguments:
     - name: total_amount
       description: "The total amount of the order."
       type: numeric
   definition: |
     {% set discount_rate = 0.1 if total_amount > 1000 else 0.05 %}
     {% set discount = total_amount * discount_rate %}
     Template:Discount

• In this example, we're defining a macro called "calculate_discount" that takes in an argument for the total amount of an order and calculates the discount for that order based on the total amount. We're using the Jinja templating language to define the macro.
  
  
  
  • Test Configuration: The test configuration is used to define tests that can be run to ensure the quality of data in a DBT project. Here's an example of what a test configuration might look like:

tests:
 - name: order_date_after_2019
   description: "Checks that all orders have a date after January 1st, 2019."
   severity: error
   tags:
     - data_quality
   meta:
     - dbt_version: '>0.19.1'
   columns:
     - orders.order_date
   tests:
     - condition: "{{ order_date >= '2019-01-01' }}"

• In this example, we're defining a test called "order_date_after_2019" that checks that all orders have a date after January 1st, 2019. We're using the "columns" and "tests" properties to define the columns that should be tested and the condition that those columns should meet. We're also defining some metadata and tags to help identify and organize the test.

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