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Supply Chain & Retail Solutions user guide
- Overview
- Platform setup and administration
- Platform setup and administration
- Platform architecture
- Data Bridge onboarding overview
- Connecting a Peak-managed data lake
- Connecting a customer-managed data lake
- Creating an AWS IAM role for Data Bridge
- Connecting a Snowflake data warehouse
- Connecting a Redshift data warehouse (public connectivity)
- Connecting a Redshift data warehouse (private connectivity)
- Reauthorizing a Snowflake OAuth connection
- Using Snowflake with Peak
- SQL Explorer overview
- Roles and permissions
- User management
- Inventory management solution
- Commercial pricing solution
- Merchandising solution
Product extra dataset containing tenant-specific additional product attributes in key-value format, extending the standard Products dataset for Rebuy & Replenishment.
The product extra dataset contains tenant-specific additional product attributes in key-value format. It allows custom product metadata to be provided beyond the standard Products dataset.
Purpose
The Rebuy & Replenishment capability uses the product extra dataset to capture custom product attributes that are specific to a tenant and not covered by the standard Products dataset. Each record stores a single attribute name and value for a product, allowing flexible extension of product metadata.
Required fields
| Field | Description | Type | Use | Notes |
|---|---|---|---|---|
product_id | Unique identifier for the product the attribute belongs to. | string | Application | N/A |
product_extra_id | Unique identifier for the extra attribute record. | string | Application | N/A |
product_extra_attribute | Name of the custom attribute. | string | Application | N/A |
product_extra_value | Value of the custom attribute. | string | Application | Optional. |
created_at | Date and time the record was created. | datetime | Application | N/A |
Usage notes
- This dataset is optional and only required when custom product attributes are needed beyond the standard Products dataset.
- Each record represents a single attribute as a key-value pair, so a product can have multiple records.
- Product identifiers must align with the corresponding Products dataset.
- Attribute names should be used consistently across records to support reliable analysis.