Macros that generate dbt code, and log it to the command line.

This macro generates lightweight YAML for a Source, which you can then paste into a schema file.

• schema_name (required): The schema name that contains your source data
• database_name (optional, default=target.database): The database that your source data is in.
• generate_columns (optional, default=False): Whether you want to add the column names to your source definition.

1. Use the macro (in dbt Develop, in a scratch file, or in a run operation) like so:

{{ codegen.generate_source('raw_jaffle_shop') }}

Alternatively, call the macro as an operation:

$ dbt run-operation generate_source --args 'schema_name: raw_jaffle_shop'

or

# for multiple arguments, use the dict syntax
$ dbt run-operation generate_source --args '{"schema_name": "raw_jaffle_shop", "database_name": "raw"}'

2. The YAML for the source will be logged to the command line

version: 2

sources:
  - name: raw_jaffle_shop
    database: raw
    tables:
      - name: customers
      - name: orders
      - name: payments

3. Paste the output in to a schema .yml file, and refactor as required.

This macro generates the SQL for a base model, which you can then paste into a model.

• source_name (required): The source you wish to generate base model SQL for.
• table_name (required): The source table you wish to generate base model SQL for.

1. Create a source for the table you wish to create a base model on top of.
2. Use the macro (in dbt Develop, or in a scratch file), and compile your code

{{
  codegen.generate_base_model(
    source_name='raw_jaffle_shop',
    table_name='customers'
  )
}}

Alternatively, call the macro as an operation:

$ dbt run-operation generate_base_model --args '{"source_name": "raw_jaffle_shop", "table_name": "customers"}'

3. The SQL for a base model will be logged to the command line

with source as (

    select * from {{ source('raw_jaffle_shop', 'customers') }}

),

renamed as (

    select
        id,
        first_name,
        last_name,
        email,
        _elt_updated_at

    from source

)

select * from renamed

4. Paste the output in to a model, and refactor as required.