Shopify (Source)

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

Models

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.

Installation Instructions

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

Configuration

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

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: []

Contributions

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.

Resources:

• Provide feedback on our existing dbt packages or what you'd like to see next
• Find all of Fivetran's pre-built dbt packages in our dbt hub
• Learn more about Fivetran in the Fivetran docs
• Check out Fivetran's blog
• Learn more about dbt in the dbt docs
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• Join the chat on Slack for live discussions and support
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