A Fulfillment Service is a third party warehouse that prepares and ships orders on behalf of the store owner. Fulfillment services charge a fee to package and ship items and update product inventory levels. Some well known fulfillment services with Shopify integrations include: Amazon, Shipwire, and Rakuten. When an app registers a new Fulfillment Service on a store, Shopify automatically creates a Location that’s associated to that fulfillment service.
The Fulfillment Services pipeline can be used to request and retrieve details of the fulfillment services from the shopify platform. Read more about this here
Configuring the Credentials
Select the account credentials which has access to relevant Shopify data from the dropdown menu & Click Next
Select the dimensions you would like to fetch from the Shopify platform. You can click on View Schema anytime to see the schema of the table being created. Each of the selected dimensions will create one or more columns in the database table in the destination warehouse.
Specifies the manner in which data will get updated in the data warehouse : UPSERT will insert only new records or records with changes and APPEND will insert all fetched data at the end. Recommended to use "Upsert" option unless there is a specific requirement.
Default Value: UPSERT
Click on + Add New for adding predicates (advanced filters)
Available predicates are as under:-
Select the type of scope you wish to get the data for. Current Client returns fulfillment providers that have been created by the app sending the request (default); and All returns all the fulfillment providers.
Scheduling specifies the frequency with which data will get updated in the data warehouse. You can choose between Manual Run, Normal Scheduling or Advance Scheduling.
- Manual Run
If scheduling is not required, you can use the toggle to run the pipeline manually.
- Normal Scheduling
Use the dropdown to select an interval-based hourly, monthly, weekly, or daily frequency.
- Advance Scheduling
Set schedules fine-grained at the level of Months, Days, Hours, and Minutes.
Detailed explanation on scheduling of pipelines can be found here
Dataset & Name
- Dataset Name
Key in the Dataset Name(also serves as the table name in your data destination).Keep in mind, that the name should be unique across the account and the data source. Special characters (except underscore _) and blank spaces are not allowed. It is best to follow a consistent naming scheme for future search to locate the tables.
- Dataset Description
Enter a short description (optional) describing the dataset being fetched by this particular pipeline.
Choose the events for which you’d like to be notified: whether "ERROR ONLY" or "ERROR AND SUCCESS".
Once you have finished click on Finish to save it. Read more about naming and saving your pipelines including the option to save them as templates here
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