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

The main focus of the package is to transform the core object tables into analytics-ready models, including a cohort model to understand how your customers are behaving over time.

This package contains transformation models, designed to work simultaneously with our Shopify source package. A dependency on the source package is declared in this package's packages.yml file, so it will automatically download when you run dbt deps. The primary outputs of this package are described below.

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

Include in your packages.yml

packages:
  - package: fivetran/shopify
    version: [">=0.5.0", "<0.6.0"]

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_schemas: ['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

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

# dbt_project.yml

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

For additional configurations for the source models, visit the Shopify source package.

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.

dbt v0.20.0 introduced a new project-level dispatch configuration that enables an "override" setting for all dispatched macros. If you are using a Databricks destination with this package you will need to add the below (or a variation of the below) dispatch configuration within your dbt_project.yml. This is required in order for the package to accurately search for macros within the dbt-labs/spark_utils then the dbt-labs/dbt_utils packages respectively.

# dbt_project.yml

dispatch:
  - macro_namespace: dbt_utils
    search_order: ['spark_utils', 'dbt_utils']

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