- 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
Region dataset containing geographic region definitions used by the Commercial Pricing solution for pricing differentiation and analysis.
The Region dataset contains geographical region definitions used by the commercial pricing solution. It enables pricing differentiation and analysis across defined geographic segments.
This dataset is required for application deployment and pricing insights analysis.
→ For the canonical technical schema (data types, validation rules, request/response examples), see Region in the API Guide.
Purpose
The commercial pricing solution uses the Region dataset to:
- Segment pricing strategies by geography
- Analyze pricing behavior across regions
- Support regional differentiation in pricing models
- Enable region-level reporting and insights
This dataset allows pricing recommendations to account for geographic variation in demand, cost, and competitive conditions.
Required fields
About the Nullable column: every field below must appear in your data. Nullable: Yes means the field can be sent as null (or left blank in your source) when no value is available; Nullable: No means a non-null value is required for every row.
| Field | Description | Type | Nullable |
|---|---|---|---|
region_id | Unique identifier for the region. | string | No |
region_name | Name of the region. | string | No |
updated_at | Timestamp when the record was last updated. | timestamp_tz | Yes |
Custom fields
The fields listed previously are the standard schema for this dataset. A field that your business requires but the standard schema does not include can be added as a custom attribute after the schema is rolled out. Custom attributes are additive, so they extend the dataset without affecting existing fields, and they are validated in the same way as standard fields.
The following request adds a REGION_MANAGER column to this dataset:
curl -X POST \
'https://ingestion.peak.ai/api/v2/schema/QP_REGION_OOTB/add-attribute' \
-H 'Authorization: YOUR_API_KEY' \
-H 'Content-Type: application/json' \
-d '{
"solutionName": "QP_OOTB",
"columnName": "REGION_MANAGER",
"dataType": "string",
"validations": [
{ "type": "optional" }
]
}'
curl -X POST \
'https://ingestion.peak.ai/api/v2/schema/QP_REGION_OOTB/add-attribute' \
-H 'Authorization: YOUR_API_KEY' \
-H 'Content-Type: application/json' \
-d '{
"solutionName": "QP_OOTB",
"columnName": "REGION_MANAGER",
"dataType": "string",
"validations": [
{ "type": "optional" }
]
}'
The table name in the request URL is this dataset's fully qualified warehouse name, which includes the prefix and suffix configured when your solution was rolled out — QP_REGION_OOTB in this example. Retrieve the name for your own solution with Describe a solution's schema.
On standard tables the API prefixes the new column name in the warehouse with C_ (Snowflake) or c_ (Redshift), so REGION_MANAGER becomes C_REGION_MANAGER and warehouse queries must use the prefixed name.
→ For the full parameter reference, supported data types, and validation rules, see Customizations in the API Guide.
Usage notes
- Regions should reflect the geographic segmentation used in pricing decisions.
- Region identifiers must align with references in related datasets (for example, Quote Line or Sales, if region-based differentiation is applied).
- Regional definitions should remain consistent over time to preserve analytical continuity.
- Historical stability of region definitions improves pricing model reliability.
Why this dataset matters
The Region dataset enables the commercial pricing solution to evaluate pricing strategies across geographic segments. Without it, pricing analysis may lack necessary geographic context and segmentation accuracy.