Shopify Transformation dbt Package (Docs)
This package models Shopify data from Fivetran's connector. It uses data in the format described by this ERD and builds off the output of our Shopify source package.
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.
The following table provides a detailed list of all models materialized within this package by default.
TIP: See more details about these models in the package's dbt docs site.
| model | description |
|---|---|
| shopify__customer_cohorts | Each record represents the monthly performance of a customer (based on customer_id), including fields for the month of their 'cohort'. |
| shopify__customers | Each record represents a distinct customer_id, with additional dimensions like lifetime value and number of orders. |
| shopify__customer_email_cohorts | Each record represents the monthly performance of a customer (based on email), including fields for the month of their 'cohort'. |
| shopify__customer_emails | Each record represents a distinct customer email, with additional dimensions like lifetime value and number of orders. |
| shopify__orders | Each record represents an order, with additional dimensions like whether it is a new or repeat purchase. |
| shopify__order_lines | Each record represents an order line item, with additional dimensions like how many items were refunded. |
| shopify__products | Each record represents a product, with additional dimensions like most recent order date and order volume. |
| shopify__transactions | Each record represents a transaction with additional calculations to handle exchange rates. |
| shopify__daily_shop | Each record represents a day of activity for each of your shops, conveyed by a suite of daily metrics about customers, orders, abandoned checkouts, fulfillment events, and more. |
| shopify__discounts | Each record represents a unique discount, enriched with information about its associated price_ruleand metrics regarding orders and abandoned checkouts. |
| shopify__inventory_levels | Each record represents an inventory level (unique pairing of inventory items and locations), enriched with information about its products, orders, and fulfillments. |
To use this dbt package, you must have the following:
- At least one Fivetran Shopify connector syncing data into your destination.
- One of the following destinations:
If you are not using the Shopify Holistic reporting package, include the following shopify package version in your packages.yml file:
TIP: Check dbt Hub for the latest installation instructions or read the dbt docs for more information on installing packages.
packages:
- package: fivetran/shopify
version: [">=0.12.0", "<0.13.0"] # we recommend using ranges to capture non-breaking changes automaticallyDo NOT include the shopify_source package in this file. The transformation package itself has a dependency on it and will install the source package as well.
If you are using a Databricks destination with this package, you must add the following (or a variation of the following) 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.
dispatch:
- macro_namespace: dbt_utils
search_order: ['spark_utils', 'dbt_utils']By default, this package runs using your destination and the shopify schema. If this is not where your Shopify data is (for example, if your Shopify schema is named shopify_fivetran), add the following configuration to your root dbt_project.yml file:
# dbt_project.yml
vars:
shopify_database: your_database_name
shopify_schema: your_schema_nameIf 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 shopify_union_schemas OR shopify_union_databases variables (cannot do both) in your root dbt_project.yml file:
# dbt_project.yml
vars:
shopify_union_schemas: ['shopify_usa','shopify_canada'] # use this if the data is in different schemas/datasets of the same database/project
shopify_union_databases: ['shopify_usa','shopify_canada'] # use this if the data is in different databases/projects but uses the same schema namePlease be aware that the native source.yml connection set up in the package will not function when the union schema/database feature is utilized. Although the data will be correctly combined, you will not observe the sources linked to the package models in the Directed Acyclic Graph (DAG). This happens because the package includes only one defined source.yml.
To connect your multiple schema/database sources to the package models, follow the steps outlined in the Union Data Defined Sources Configuration section of the Fivetran Utils documentation for the union_data macro. This will ensure a proper configuration and correct visualization of connections in the DAG.
The package takes into consideration that not every Shopify connector may have fulfillment_event data enabled. However, this table does hold valuable information that is leveraged in the shopify__daily_shop model. fulfillment_event data is disabled by default.
Add the following variable to your dbt_project.yml file to enable the modeling of fulfillment events:
# dbt_project.yml
vars:
shopify_using_fulfillment_event: true # false by defaultBy default, the data in your Shopify schema is in UTC. However, you may want reporting to reflect a specific timezone for more realistic analysis or data validation.
To convert the timezone of all timestamps in the package, update the shopify_timezone variable to your target zone in IANA tz Database format:
# dbt_project.yml
vars:
shopify_timezone: "America/New_York" # Replace with your timezoneNote: This will only numerically convert timestamps to your target timezone. They will however have a "UTC" appended to them. This is a current limitation of the dbt-date
convert_timezonemacro we leverage.
Expand/Collapse details
This package includes all source columns defined in the macros folder. You can add more columns using our pass-through column variables. These variables allow for the pass-through fields to be aliased (alias) and casted (transform_sql) if desired, but not required. Datatype casting is configured via a sql snippet within the transform_sql key. You may add the desired sql while omitting the as field_name at the end and your custom pass-though fields will be casted accordingly. Use the below format for declaring the respective pass-through variables:
# dbt_project.yml
vars:
shopify_source:
customer_pass_through_columns:
- name: "customer_custom_field"
alias: "customer_field"
order_line_refund_pass_through_columns:
- name: "unique_string_field"
alias: "field_id"
transform_sql: "cast(field_id as string)"
order_line_pass_through_columns:
- name: "that_field"
order_pass_through_columns:
- name: "sub_field"
alias: "subsidiary_field"
product_pass_through_columns:
- name: "this_field"
product_variant_pass_through_columns:
- name: "new_custom_field"
alias: "custom_field"In May 2021 the Shopify connector included support for the metafield resource. If you would like to take advantage of these metafields, this package offers corresponding mapping models which append these metafields to the respective source object for the following tables: collection, customer, order, product_image, product, product_variant, shop. If enabled, these models will materialize as shopify__[object]_metafields for each respective supported object. To enable these metafield mapping models, you may use the following configurations within your dbt_project.yml.
Note: These metafield models will contain all the same records as the corresponding staging models with the exception of the metafield columns being added.
vars:
shopify_using_all_metafields: True ## False by default. Will enable ALL metafield models. FYI - This will override all other metafield variables.
shopify_using_collection_metafields: True ## False by default. Will enable ONLY the collection metafield model.
shopify_using_customer_metafields: True ## False by default. Will enable ONLY the customer metafield model.
shopify_using_order_metafields: True ## False by default. Will enable ONLY the order metafield model.
shopify_using_product_metafields: True ## False by default. Will enable ONLY the product metafield model.
shopify_using_product_image_metafields: True ## False by default. Will enable ONLY the product image metafield model.
shopify_using_product_variant_metafields: True ## False by default. Will enable ONLY the product variant metafield model.
shopify_using_shop_metafields: True ## False by default. Will enable ONLY the shop metafield model.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_schemaIf an individual source table has a different name than the package expects, add the table name as it appears in your destination to the respective variable:
IMPORTANT: See this project's
dbt_project.ymlvariable declarations to see the expected names.
# dbt_project.yml
vars:
shopify_<default_source_table_name>_identifier: your_table_name Records from the source can sometimes arrive late. Since several of the models in this package are incremental, by default we look back 7 days to ensure late arrivals are captured while avoiding the need for frequent full refreshes. While the frequency can be reduced, we still recommend running dbt --full-refresh periodically to maintain data quality of the models. For more information on our incremental decisions, see the Incremental Strategy section of the DECISIONLOG.
To change the default lookback window, add the following variable to your dbt_project.yml file:
vars:
shopify:
lookback_window: number_of_days # default is 7Expand for details
Fivetran offers the ability for you to orchestrate your dbt project through Fivetran Transformations for dbt Core™. Learn how to set up your project for orchestration through Fivetran in our Transformations for dbt Core setup guides.
This dbt package is dependent on the following dbt packages. Please be aware that these dependencies are installed by default within this package. For more information on the following packages, refer to the dbt hub site.
IMPORTANT: If you have any of these dependent packages in your own
packages.ymlfile, we highly recommend that you remove them from your rootpackages.ymlto avoid package version conflicts.
packages:
- package: fivetran/shopify_source
version: [">=0.12.0", "<0.13.0"]
- package: fivetran/fivetran_utils
version: [">=0.4.0", "<0.5.0"]
- package: dbt-labs/dbt_utils
version: [">=1.0.0", "<2.0.0"]
- package: calogica/dbt_date
version: [">=0.9.0", "<1.0.0"]
- package: dbt-labs/spark_utils
version: [">=0.3.0", "<0.4.0"]The Fivetran team maintaining this package only maintains the latest version of the package. We highly recommend you stay consistent with the latest version of the package and refer to the CHANGELOG and release notes for more information on changes across versions.
A small team of analytics engineers at Fivetran develops these dbt packages. However, the packages are made better by community contributions!
We highly encourage and welcome contributions to this package. Check out this dbt Discourse article on the best workflow for contributing to a package!
- If you have questions or want to reach out for help, please refer to the GitHub Issue section to find the right avenue of support for you.
- If you would like to provide feedback to the dbt package team at Fivetran or would like to request a new dbt package, fill out our Feedback Form.
- Have questions or want to be part of the community discourse? Create a post in the Fivetran community and our team along with the community can join in on the discussion!