This package models Shopify data from Fivetran's connector. It uses data in the format described by this ERD.

This package enriches your Fivetran data by doing the following:

• Adds descriptions to tables and columns that are synced using Fivetran
• Adds column-level testing where applicable. For example, all primary keys are tested for uniqueness and non-null values.
• Models staging tables, which will be used in our transform package

This package contains staging models, designed to work simultaneously with our Shopify modeling package. The staging models name columns consistently across all packages:

• Boolean fields are prefixed with is_ or has_
• Timestamps are appended with _at
• ID primary keys are prefixed with the name of the table. For example, the campaign table's ID column is renamed campaign_id.

Check dbt Hub for the latest installation instructions, or read the dbt docs for more information on installing packages.

By default, this package looks for your Shopify data in the shopify schema of your target database. If this is not where your Shopify data is, add the following configuration to your dbt_project.yml file:

# dbt_project.yml

...
config-version: 2

vars:
    shopify_database: your_database_name
    shopify_schema: your_schema_name

If you have multiple Shopify connectors in Fivetran and would like to use this package on all of them simultaneously, we have provided functionality to do so. The package will union all of the data together and pass the unioned table into the transformations. You will be able to see which source it came from in the source_relation column of each model. To use this functionality, you will need to set either the union_schemas or union_databases variables:

# dbt_project.yml

...
config-version: 2

vars:
    union_schema: ['shopify_usa','shopify_canada'] # use this if the data is in different schemas/datasets of the same database/project
    union_databases: ['shopify_usa','shopify_canada'] # use this if the data is in different databases/projects but uses the same schema name

This package includes all source columns defined in the staging_columns.sql macro. To add additional columns to this package, do so using our pass-through column variables. This is extremely useful if you'd like to include custom fields to the package.

# dbt_project.yml

...
config-version: 2

vars:
  shopify_source:
    customer_pass_through_columns: []
    order_line_refund_pass_through_columns: []
    order_line_pass_through_columns: []
    order_pass_through_columns: []
    product_pass_through_columns: []
    product_variant_pass_through_columns: []

By default this package will build the Shopify staging models within a schema titled (<target_schema> + _stg_shopify) in your target database. If this is not where you would like your staging Shopify data to be written to, add the following configuration to your dbt_project.yml file:

# dbt_project.yml

...
models:
  shopify_source:
    +schema: my_new_schema_name # leave blank for just the target_schema

Additional contributions to this package are very welcome! Please create issues or open PRs against master. Check out this post on the best workflow for contributing to a package.

This package has been tested on BigQuery, Snowflake, Redshift, Postgres, and Databricks.

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