Source Systems & Freshness
Technical reference for DataHawk source systems, schemas, account requirements, and freshness fields.
Use this page when you need to map a business data type to its source API, account requirement, schema, or freshness field. For a business-friendly explanation of public vs. private data, see How DataHawk Sources Your Data.
Schema mapping by data type
DataHawk organizes datasets into schemas based on source system and modeling layer.
| Data type | Primary schemas | Account required |
|---|---|---|
| Product listings, prices, Buy Box, sales estimates | PRODUCT, MARKET, REPORTS | No account connection for tracked public data |
| Keyword rankings and search volume | SEO, REPORTS | No account connection, but keyword tracking is required |
| Category and market data | MARKET, REPORTS | No account connection, but category tracking is required |
| Seller orders, finance, fees, profit, returns | FINANCE, SELLING_PARTNER | Seller Central account |
| Inventory and FBA movement data | SELLING_PARTNER, RAW_INVENTORY | Seller Central account |
| Vendor sales, inventory, and purchase-order data | SELLING_PARTNER, REPORTS | Vendor Central account |
| Amazon and Walmart advertising | ADVERTISING | Advertising account |
| Marketplace, account, currency, and tracked-entity references | REFERENTIAL | Varies by table |
For the full schema map, see Technical Reference.
Source systems
| Source system | What DataHawk collects |
|---|---|
| Amazon public storefront | Product attributes, pricing, BSR, offers, category rank, and search visibility signals |
| Amazon Selling Partner API (SP-API) | Seller, Vendor, inventory, finance, returns, replenishment, and Brand Analytics reports |
| Amazon Advertising API | Sponsored Ads and DSP performance data |
| Walmart Marketplace API | Walmart Marketplace seller data |
| Walmart Connect API | Walmart advertising performance data |
| DataHawk collection and modeling layer | Sales estimates, competitor detection, listing quality analysis, keyword research, and curated report tables |
Freshness and delay fields
Many tables expose one or more fields that help you determine whether a row is current, historical, or recently refreshed.
| Field pattern | Typical use |
|---|---|
DATE_DAY / OBSERVATION_DATE | Business date represented by the row |
OBSERVATION_TIME | Timestamp of a collected snapshot when more precision is available |
UPDATED_AT | Time when the row was last updated in DataHawk |
TIME_INTERVAL_START_DATE / TIME_INTERVAL_END_DATE | Start and end of an aggregated period |
REPORTING_DATE / report date fields | Source-report date from Amazon or Walmart |
For current-state tables such as inventory or listing snapshots, filter to the latest available date instead of querying all history. For event or ledger tables, use the business date and transaction status fields documented in the relevant dataset page.