To use this dbt package, you must have the following:
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']
shopify
transformation package)If you are not using the Shopify transformation package, include the following 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_source version: [">=0.12.0", "<0.13.0"] # we recommend using ranges to capture non-breaking changes automatically
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
and your issue
table is named usa_issue
), add the following configuration to your root dbt_project.yml
file:
vars:
shopify_database: your_destination_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 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 name
NOTE: 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 definedsource.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.
fulfillment_event
dataThe 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 in the transformation package. 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 default
By 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 timezone
Note: 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_timezone
macro we leverage.
This dbt package is dependent on the following dbt packages. 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.yml
file, we highly recommend that you remove them from your rootpackages.yml
to avoid package version conflicts.packages: - 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"]
## How is this package maintained and can I contribute?
### Package Maintenance
The Fivetran team maintaining this package _only_ maintains the latest version of the package. We highly recommend that you stay consistent with the [latest version](https://hub.getdbt.com/fivetran/shopify_source/latest/) of the package and refer to the [CHANGELOG](https://github.com/fivetran/dbt_shopify_source/blob/main/CHANGELOG.md) and release notes for more information on changes across versions.
### Contributions
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](https://discourse.getdbt.com/t/contributing-to-a-dbt-package/657) to learn how to contribute to a dbt package.
## Are there any resources available?
- If you have questions or want to reach out for help, see the [GitHub Issue](https://github.com/fivetran/dbt_shopify_source/issues/new/choose) 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](https://www.surveymonkey.com/r/DQ7K7WW).